import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings
filterwarnings('ignore')
plt.rcParams['figure.figsize'] = (12,7)
import scipy
from scipy import stats
from scipy.stats import ttest_ind
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.formula.api import ols
from statsmodels.stats.multicomp import pairwise_tukeyhsd
data = pd.read_csv("D:/Machine Learning/Project/HousePrices.csv")
data.sample(10)
| Id | Dwell_Type | Zone_Class | LotFrontage | LotArea | Road_Type | Alley | Property_Shape | LandContour | Utilities | ... | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | Property_Sale_Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1664 | 1665 | 50 | RL | 107.0 | 8758 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | GdWo | NaN | 0 | 5 | 2007 | WD | Normal | 112000 |
| 1744 | 1745 | 60 | RL | 75.0 | 8168 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 7 | 2008 | WD | Normal | 222500 |
| 1607 | 1608 | 50 | RL | 103.0 | 6998 | Pave | NaN | Reg | Low | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 5 | 2006 | WD | Normal | 160000 |
| 136 | 137 | 20 | RL | NaN | 10355 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 7 | 2007 | WD | Normal | 143000 |
| 1928 | 974 | 20 | FV | 95.0 | 11639 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | New | Partial | 182000 |
| 1028 | 1029 | 50 | RL | 79.0 | 9492 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 4 | 2007 | WD | Normal | 105000 |
| 2046 | 920 | 20 | RL | 87.0 | 11029 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 5 | 2008 | WD | Normal | 176500 |
| 1119 | 1120 | 20 | RL | 70.0 | 7560 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | GdWo | NaN | 0 | 7 | 2006 | WD | Normal | 133700 |
| 1054 | 1055 | 60 | RL | 90.0 | 11367 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 11 | 2006 | WD | Normal | 255000 |
| 783 | 784 | 85 | RL | NaN | 9101 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 7 | 2009 | WD | Normal | 165500 |
10 rows × 81 columns
data.drop(['Id'],axis = 1,inplace = True)
data.shape
(2073, 80)
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LotFrontage 1753 non-null float64 3 LotArea 2073 non-null int64 4 Road_Type 2073 non-null object 5 Alley 129 non-null object 6 Property_Shape 2073 non-null object 7 LandContour 2073 non-null object 8 Utilities 2073 non-null object 9 LotConfig 2073 non-null object 10 LandSlope 2073 non-null object 11 Neighborhood 2073 non-null object 12 Condition1 2073 non-null object 13 Condition2 2073 non-null object 14 Dwelling_Type 2073 non-null object 15 HouseStyle 2073 non-null object 16 OverallQual 2073 non-null int64 17 OverallCond 2073 non-null int64 18 YearBuilt 2073 non-null int64 19 YearRemodAdd 2073 non-null int64 20 RoofStyle 2073 non-null object 21 RoofMatl 2073 non-null object 22 Exterior1st 2073 non-null object 23 Exterior2nd 2073 non-null object 24 MasVnrType 2059 non-null object 25 MasVnrArea 2059 non-null float64 26 ExterQual 2073 non-null object 27 ExterCond 2073 non-null object 28 Foundation 2073 non-null object 29 BsmtQual 2014 non-null object 30 BsmtCond 2014 non-null object 31 BsmtExposure 2012 non-null object 32 BsmtFinType1 2014 non-null object 33 BsmtFinSF1 2073 non-null int64 34 BsmtFinType2 2013 non-null object 35 BsmtFinSF2 2073 non-null int64 36 BsmtUnfSF 2073 non-null int64 37 TotalBsmtSF 2073 non-null int64 38 Heating 2073 non-null object 39 HeatingQC 2073 non-null object 40 CentralAir 2073 non-null object 41 Electrical 2072 non-null object 42 1stFlrSF 2073 non-null int64 43 2ndFlrSF 2073 non-null int64 44 LowQualFinSF 2073 non-null int64 45 GrLivArea 2073 non-null int64 46 BsmtFullBath 2073 non-null int64 47 BsmtHalfBath 2073 non-null int64 48 FullBath 2073 non-null int64 49 HalfBath 2073 non-null int64 50 BedroomAbvGr 2073 non-null int64 51 KitchenAbvGr 2073 non-null int64 52 KitchenQual 2073 non-null object 53 TotRmsAbvGrd 2073 non-null int64 54 Functional 2073 non-null object 55 Fireplaces 2073 non-null int64 56 FireplaceQu 1085 non-null object 57 GarageType 1960 non-null object 58 GarageYrBlt 1960 non-null float64 59 GarageFinish 1960 non-null object 60 GarageCars 2073 non-null int64 61 GarageArea 2073 non-null int64 62 GarageQual 1960 non-null object 63 GarageCond 1960 non-null object 64 PavedDrive 2073 non-null object 65 WoodDeckSF 2073 non-null int64 66 OpenPorchSF 2073 non-null int64 67 EnclosedPorch 2073 non-null int64 68 3SsnPorch 2073 non-null int64 69 ScreenPorch 2073 non-null int64 70 PoolArea 2073 non-null int64 71 PoolQC 8 non-null object 72 Fence 404 non-null object 73 MiscFeature 80 non-null object 74 MiscVal 2073 non-null int64 75 MoSold 2073 non-null int64 76 YrSold 2073 non-null int64 77 SaleType 2073 non-null object 78 SaleCondition 2073 non-null object 79 Property_Sale_Price 2073 non-null int64 dtypes: float64(3), int64(34), object(43) memory usage: 1.3+ MB
data = data.rename(columns = {'1stFlrSF' : 'FirstFlrSF', '2ndFlrSF' : 'SecondFlrSF', '3SsnPorch' : 'ThrSsnPorch'})
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LotFrontage 1753 non-null float64 3 LotArea 2073 non-null int64 4 Road_Type 2073 non-null object 5 Alley 129 non-null object 6 Property_Shape 2073 non-null object 7 LandContour 2073 non-null object 8 Utilities 2073 non-null object 9 LotConfig 2073 non-null object 10 LandSlope 2073 non-null object 11 Neighborhood 2073 non-null object 12 Condition1 2073 non-null object 13 Condition2 2073 non-null object 14 Dwelling_Type 2073 non-null object 15 HouseStyle 2073 non-null object 16 OverallQual 2073 non-null int64 17 OverallCond 2073 non-null int64 18 YearBuilt 2073 non-null int64 19 YearRemodAdd 2073 non-null int64 20 RoofStyle 2073 non-null object 21 RoofMatl 2073 non-null object 22 Exterior1st 2073 non-null object 23 Exterior2nd 2073 non-null object 24 MasVnrType 2059 non-null object 25 MasVnrArea 2059 non-null float64 26 ExterQual 2073 non-null object 27 ExterCond 2073 non-null object 28 Foundation 2073 non-null object 29 BsmtQual 2014 non-null object 30 BsmtCond 2014 non-null object 31 BsmtExposure 2012 non-null object 32 BsmtFinType1 2014 non-null object 33 BsmtFinSF1 2073 non-null int64 34 BsmtFinType2 2013 non-null object 35 BsmtFinSF2 2073 non-null int64 36 BsmtUnfSF 2073 non-null int64 37 TotalBsmtSF 2073 non-null int64 38 Heating 2073 non-null object 39 HeatingQC 2073 non-null object 40 CentralAir 2073 non-null object 41 Electrical 2072 non-null object 42 FirstFlrSF 2073 non-null int64 43 SecondFlrSF 2073 non-null int64 44 LowQualFinSF 2073 non-null int64 45 GrLivArea 2073 non-null int64 46 BsmtFullBath 2073 non-null int64 47 BsmtHalfBath 2073 non-null int64 48 FullBath 2073 non-null int64 49 HalfBath 2073 non-null int64 50 BedroomAbvGr 2073 non-null int64 51 KitchenAbvGr 2073 non-null int64 52 KitchenQual 2073 non-null object 53 TotRmsAbvGrd 2073 non-null int64 54 Functional 2073 non-null object 55 Fireplaces 2073 non-null int64 56 FireplaceQu 1085 non-null object 57 GarageType 1960 non-null object 58 GarageYrBlt 1960 non-null float64 59 GarageFinish 1960 non-null object 60 GarageCars 2073 non-null int64 61 GarageArea 2073 non-null int64 62 GarageQual 1960 non-null object 63 GarageCond 1960 non-null object 64 PavedDrive 2073 non-null object 65 WoodDeckSF 2073 non-null int64 66 OpenPorchSF 2073 non-null int64 67 EnclosedPorch 2073 non-null int64 68 ThrSsnPorch 2073 non-null int64 69 ScreenPorch 2073 non-null int64 70 PoolArea 2073 non-null int64 71 PoolQC 8 non-null object 72 Fence 404 non-null object 73 MiscFeature 80 non-null object 74 MiscVal 2073 non-null int64 75 MoSold 2073 non-null int64 76 YrSold 2073 non-null int64 77 SaleType 2073 non-null object 78 SaleCondition 2073 non-null object 79 Property_Sale_Price 2073 non-null int64 dtypes: float64(3), int64(34), object(43) memory usage: 1.3+ MB
data.describe()
| Dwell_Type | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | ... | WoodDeckSF | OpenPorchSF | EnclosedPorch | ThrSsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | Property_Sale_Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2073.000000 | 1753.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2059.000000 | 2073.000000 | 2073.000000 | ... | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 | 2073.000000 |
| mean | 60.556199 | 72.056475 | 10717.853353 | 6.233478 | 5.835022 | 1969.445731 | 1986.617463 | 101.875668 | 437.949349 | 49.219489 | ... | 94.168837 | 46.035697 | 21.029908 | 2.979257 | 14.552340 | 2.299083 | 39.736614 | 6.287506 | 2007.851905 | 180967.284129 |
| std | 159.924810 | 27.987475 | 9215.982306 | 1.517115 | 1.360214 | 30.222647 | 20.432694 | 179.169217 | 448.051119 | 165.621465 | ... | 129.072113 | 64.768713 | 59.569928 | 26.817218 | 54.099626 | 37.411031 | 429.819512 | 2.690130 | 1.329705 | 78666.411112 |
| min | 20.000000 | 21.000000 | 1300.000000 | 1.000000 | 1.000000 | 1872.000000 | 1950.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 2006.000000 | 34900.000000 |
| 25% | 20.000000 | 60.000000 | 7620.000000 | 5.000000 | 5.000000 | 1950.000000 | 1970.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 5.000000 | 2007.000000 | 130000.000000 |
| 50% | 50.000000 | 70.000000 | 9492.000000 | 6.000000 | 5.000000 | 1971.000000 | 1995.000000 | 0.000000 | 381.000000 | 0.000000 | ... | 0.000000 | 24.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 2008.000000 | 163990.000000 |
| 75% | 70.000000 | 83.000000 | 11601.000000 | 7.000000 | 7.000000 | 1999.000000 | 2004.000000 | 161.500000 | 708.000000 | 0.000000 | ... | 168.000000 | 67.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 8.000000 | 2009.000000 | 214000.000000 |
| max | 7080.000000 | 313.000000 | 215245.000000 | 10.000000 | 9.000000 | 2010.000000 | 2023.000000 | 1600.000000 | 5644.000000 | 1474.000000 | ... | 857.000000 | 547.000000 | 552.000000 | 508.000000 | 480.000000 | 738.000000 | 15500.000000 | 12.000000 | 2010.000000 | 755000.000000 |
8 rows × 37 columns
data.describe(include='O')
| Zone_Class | Road_Type | Alley | Property_Shape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | ... | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | PoolQC | Fence | MiscFeature | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2073 | 2073 | 129 | 2073 | 2073 | 2073 | 2073 | 2073 | 2073 | 2073 | ... | 1960 | 1960 | 1960 | 1960 | 2073 | 8 | 404 | 80 | 2073 | 2073 |
| unique | 5 | 2 | 2 | 4 | 4 | 2 | 5 | 3 | 25 | 9 | ... | 6 | 3 | 5 | 5 | 3 | 3 | 4 | 4 | 9 | 6 |
| top | RL | Pave | Grvl | Reg | Lvl | AllPub | Inside | Gtl | NAmes | Norm | ... | Attchd | Unf | TA | TA | Y | Gd | MnPrv | Shed | WD | Normal |
| freq | 1611 | 2063 | 72 | 1300 | 1854 | 2070 | 1481 | 1958 | 317 | 1794 | ... | 1252 | 857 | 1870 | 1885 | 1913 | 4 | 233 | 74 | 1797 | 1695 |
4 rows × 43 columns
# Check which variables have missing values
data.isnull().sum()
Dwell_Type 0
Zone_Class 0
LotFrontage 320
LotArea 0
Road_Type 0
...
MoSold 0
YrSold 0
SaleType 0
SaleCondition 0
Property_Sale_Price 0
Length: 80, dtype: int64
sns.heatmap(data.isnull(), cmap = 'Paired_r')
plt.title('Heat Map of Nulls')
Text(0.5, 1.0, 'Heat Map of Nulls')
data.Property_Sale_Price.isnull().sum()
0
data.Property_Sale_Price.describe()
count 2073.000000 mean 180967.284129 std 78666.411112 min 34900.000000 25% 130000.000000 50% 163990.000000 75% 214000.000000 max 755000.000000 Name: Property_Sale_Price, dtype: float64
data.Property_Sale_Price.value_counts()
140000 31
135000 28
110000 23
160000 22
155000 20
..
412500 1
306000 1
195400 1
283463 1
250580 1
Name: Property_Sale_Price, Length: 663, dtype: int64
data.Property_Sale_Price.value_counts().sum()
2073
plt.hist(data.Property_Sale_Price)
plt.title('Hist of Property_Sale_Price')
Text(0.5, 1.0, 'Hist of Property_Sale_Price')
sns.distplot(data.Property_Sale_Price)
plt.title('Normality of Property_Sale_Price')
Text(0.5, 1.0, 'Normality of Property_Sale_Price')
sns.countplot(data.Property_Sale_Price)
plt.title('Count plot of Property_Sale_Price')
Text(0.5, 1.0, 'Count plot of Property_Sale_Price')
sns.boxplot(x = data.Property_Sale_Price, data =data, color = 'r')
plt.title('Box plot of Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of Property_Sale_Price')
IQR = data.Property_Sale_Price.quantile(.75) - data.Property_Sale_Price.quantile(.25)
IQR
84000.0
upper_limit = data.Property_Sale_Price.quantile(.75) + (1.5*IQR)
upper_limit
340000.0
lower_limit = data.Property_Sale_Price.quantile(.25) - (1.5*IQR)
lower_limit
4000.0
len(data.Property_Sale_Price[data.Property_Sale_Price > upper_limit])
78
len(data.Property_Sale_Price[data.Property_Sale_Price < lower_limit])
0
for i in np.arange(upper_limit,800000,100000):
outliers = len(data.Property_Sale_Price[data.Property_Sale_Price > i])
print('At a limit of :', i, 'There are', outliers, 'outliers')
At a limit of : 340000.0 There are 78 outliers At a limit of : 440000.0 There are 21 outliers At a limit of : 540000.0 There are 11 outliers At a limit of : 640000.0 There are 2 outliers At a limit of : 740000.0 There are 2 outliers
data['Property_Sale_Price'] = np.where(data['Property_Sale_Price'] > upper_limit, upper_limit, data['Property_Sale_Price'])
sns.boxplot(x = data.Property_Sale_Price, data =data, color = 'g')
plt.title('Box plot of Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of Property_Sale_Price')
sns.distplot(data.Property_Sale_Price)
plt.title('Normality of Property_Sale_Price')
Text(0.5, 1.0, 'Normality of Property_Sale_Price')
sns.violinplot(data.Property_Sale_Price, color = 'pink')
plt.title('Violinplot Of Property_Sale_Price')
Text(0.5, 1.0, 'Violinplot Of Property_Sale_Price')
data.Dwell_Type.isnull().sum()
0
data.Dwell_Type.describe()
count 2073.000000 mean 60.556199 std 159.924810 min 20.000000 25% 20.000000 50% 50.000000 75% 70.000000 max 7080.000000 Name: Dwell_Type, dtype: float64
data.Dwell_Type.value_counts()
20 722 60 431 50 205 30 133 120 114 90 91 80 78 160 78 70 77 190 42 85 34 75 19 180 14 150 13 45 13 40 7 303 1 7080 1 Name: Dwell_Type, dtype: int64
data["Dwell_Type"]=data["Dwell_Type"].replace([303,7080],[30,70])
data["Dwell_Type"].value_counts()
20 722 60 431 50 205 30 134 120 114 90 91 70 78 80 78 160 78 190 42 85 34 75 19 180 14 150 13 45 13 40 7 Name: Dwell_Type, dtype: int64
data["Dwell_Type"].value_counts().sum()
2073
sns.countplot(data.Dwell_Type)
plt.title('Count plot of Dwell_Type')
Text(0.5, 1.0, 'Count plot of Dwell_Type')
data.groupby("Dwell_Type")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Dwell_Type | ||||||||
| 20 | 722.0 | 180192.246537 | 66834.894527 | 35311.0 | 132000.0 | 159250.0 | 217875.0 | 340000.0 |
| 30 | 134.0 | 124344.089552 | 59968.092222 | 34900.0 | 86000.0 | 109700.0 | 136825.0 | 340000.0 |
| 40 | 7.0 | 177600.000000 | 70304.125057 | 79500.0 | 133000.0 | 152000.0 | 242850.0 | 260000.0 |
| 45 | 13.0 | 110961.538462 | 21171.306780 | 76000.0 | 96500.0 | 109500.0 | 132000.0 | 139400.0 |
| 50 | 205.0 | 147919.863415 | 57580.464374 | 37900.0 | 114500.0 | 135000.0 | 164000.0 | 340000.0 |
| 60 | 431.0 | 226878.104408 | 60349.414027 | 35311.0 | 180500.0 | 214000.0 | 267500.0 | 340000.0 |
| 70 | 78.0 | 170636.205128 | 55594.426663 | 40000.0 | 130500.0 | 161000.0 | 195500.0 | 340000.0 |
| 75 | 19.0 | 180131.578947 | 73441.178675 | 101000.0 | 123250.0 | 163000.0 | 205750.0 | 340000.0 |
| 80 | 78.0 | 170040.769231 | 41372.951945 | 107000.0 | 145000.0 | 165250.0 | 180000.0 | 340000.0 |
| 85 | 34.0 | 146567.647059 | 18473.006032 | 123000.0 | 133225.0 | 140750.0 | 154000.0 | 198500.0 |
| 90 | 91.0 | 137172.824176 | 36641.835926 | 75000.0 | 118250.0 | 136500.0 | 145900.0 | 268000.0 |
| 120 | 114.0 | 200466.263158 | 53764.663148 | 99500.0 | 162500.0 | 192250.0 | 221750.0 | 340000.0 |
| 150 | 13.0 | 190773.076923 | 71186.924172 | 127500.0 | 143750.0 | 153500.0 | 236500.0 | 319900.0 |
| 160 | 78.0 | 139492.538462 | 33678.229767 | 75000.0 | 115750.0 | 147200.0 | 165000.0 | 239000.0 |
| 180 | 14.0 | 103785.714286 | 28187.363703 | 75500.0 | 81875.0 | 91500.0 | 133250.0 | 148000.0 |
| 190 | 42.0 | 130161.904762 | 32290.716044 | 55000.0 | 113000.0 | 126250.0 | 143750.0 | 228950.0 |
mod = ols('Property_Sale_Price ~ Dwell_Type', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Dwell_Type 1.0 5.534241e+10 5.534241e+10 12.386042 0.000442 Residual 2071.0 9.253492e+12 4.468127e+09 NaN NaN
tukey_Dwell_Type = pairwise_tukeyhsd(data.Property_Sale_Price, data.Dwell_Type, alpha = 0.05)
print(tukey_Dwell_Type)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
20 30 -55848.157 0.001 -74810.1603 -36886.1536 True
20 40 -2592.2465 0.9 -79154.4599 73969.9668 False
20 45 -69230.7081 0.0028 -125642.7382 -12818.6779 True
20 50 -32272.3831 0.001 -48226.1291 -16318.6372 True
20 60 46685.8579 0.001 34414.9766 58956.7392 True
20 70 -9556.0414 0.9 -33582.9241 14470.8413 False
20 75 -60.6676 0.9 -46913.0888 46791.7537 False
20 80 -10151.4773 0.9 -34178.36 13875.4054 False
20 85 -33624.5995 0.0844 -69001.6136 1752.4147 False
20 90 -43019.4224 0.001 -65444.0021 -20594.8426 True
20 120 20274.0166 0.0512 -42.5562 40590.5894 False
20 150 10580.8304 0.9 -45831.1998 66992.8605 False
20 160 -40699.7081 0.001 -64726.5908 -16672.8254 True
20 180 -76406.5323 0.001 -130803.4917 -22009.5728 True
20 190 -50030.3418 0.001 -82028.2634 -18032.4202 True
30 40 53255.9104 0.5812 -24902.6356 131414.4565 False
30 45 -13382.5511 0.9 -71942.8017 45177.6995 False
30 50 23575.7739 0.0275 1181.4036 45970.1441 True
30 60 102534.0149 0.001 82595.0997 122472.93 True
30 70 46292.1156 0.001 17581.8927 75002.3385 True
30 75 55787.4894 0.0107 6369.5252 105205.4536 True
30 80 45696.6797 0.001 16986.4568 74406.9026 True
30 85 22223.5575 0.8191 -16487.1508 60934.2658 False
30 90 12828.7346 0.9 -14554.6104 40212.0796 False
30 120 76122.1736 0.001 50436.5952 101807.752 True
30 150 66428.9874 0.01 7868.7368 124989.238 True
30 160 15148.4489 0.9 -13561.774 43858.6718 False
30 180 -20558.3753 0.9 -77180.0584 36063.3079 False
30 190 5817.8152 0.9 -29831.2071 41466.8376 False
40 45 -66638.4615 0.5286 -161145.1697 27868.2467 False
40 50 -29680.1366 0.9 -107163.8301 47803.5569 False
40 60 49278.1044 0.6695 -27531.8902 126088.0991 False
40 70 -6963.7949 0.9 -86503.0421 72575.4523 False
40 75 2531.5789 0.9 -86599.505 91662.6629 False
40 80 -7559.2308 0.9 -87098.4779 71980.0164 False
40 85 -31032.3529 0.9 -114702.7412 52638.0354 False
40 90 -40427.1758 0.9 -119497.1608 38642.8092 False
40 120 22866.2632 0.9 -55631.9144 101364.4407 False
40 150 13173.0769 0.9 -81333.6313 107679.7851 False
40 160 -38107.4615 0.9 -117646.7087 41431.7856 False
40 180 -73814.2857 0.3225 -167132.183 19503.6115 False
40 190 -47438.0952 0.8139 -129736.7449 34860.5544 False
45 50 36958.325 0.6707 -20698.1352 94614.7851 False
45 60 115916.5659 0.001 59168.7034 172664.4285 True
45 70 59674.6667 0.057 -716.0319 120065.3652 False
45 75 69170.0405 0.0821 -3389.554 141729.635 False
45 80 59079.2308 0.0634 -1311.4678 119469.9293 False
45 85 35606.1086 0.891 -30130.3235 101342.5407 False
45 90 26211.2857 0.9 -33560.0047 85982.5761 False
45 120 89504.7247 0.001 30491.9421 148517.5073 True
45 150 79811.5385 0.0452 741.5535 158881.5235 True
45 160 28531.0 0.9 -31859.6986 88921.6986 False
45 180 -7175.8242 0.9 -84821.008 70469.3597 False
45 190 19200.3663 0.9 -44780.9776 83181.7102 False
50 60 78958.241 0.001 61854.8853 96061.5967 True
50 70 22716.3417 0.2131 -4102.3335 49535.0169 False
50 75 32211.7155 0.6146 -16131.8427 80555.2738 False
50 80 22120.9058 0.253 -4697.7694 48939.581 False
50 85 -1352.2164 0.9 -38681.6066 35977.1739 False
50 90 -10747.0392 0.9 -36140.1884 14646.1099 False
50 120 52546.3997 0.001 28994.0458 76098.7537 True
50 150 42853.2135 0.4401 -14803.2466 100509.6736 False
50 160 -8427.325 0.9 -35246.0002 18391.3502 False
50 180 -44134.1491 0.3193 -99820.5877 11552.2895 False
50 190 -17757.9587 0.9 -51902.0245 16386.1072 False
60 70 -56241.8993 0.001 -81047.0168 -31436.7817 True
60 75 -46746.5255 0.0563 -94002.7646 509.7137 False
60 80 -56837.3352 0.001 -81642.4527 -32032.2176 True
60 85 -80310.4573 0.001 -116220.5659 -44400.3488 True
60 90 -89705.2802 0.001 -112961.7748 -66448.7856 True
60 120 -26411.8413 0.0022 -47643.0893 -5180.5932 True
60 150 -36105.0275 0.6813 -92852.8901 20642.8351 False
60 160 -87385.5659 0.001 -112190.6835 -62580.4484 True
60 180 -123092.3901 0.001 -177837.5448 -68347.2354 True
60 190 -96716.1996 0.001 -129302.5427 -64129.8566 True
70 75 9495.3738 0.9 -42078.5412 61069.2888 False
70 80 -595.4359 0.9 -32875.6221 31684.7503 False
70 85 -24068.5581 0.8038 -65496.2355 17359.1194 False
70 90 -33463.381 0.0209 -64569.3467 -2357.4152 True
70 120 29830.058 0.0464 207.7276 59452.3885 True
70 150 20136.8718 0.9 -40253.8268 80527.5704 False
70 160 -31143.6667 0.073 -63423.8529 1136.5195 False
70 180 -66850.4908 0.009 -125363.2974 -8337.6843 True
70 190 -40474.3004 0.0288 -79056.5024 -1892.0983 True
75 80 -10090.8097 0.9 -61664.7247 41483.1053 False
75 85 -33563.9319 0.8031 -91305.691 24177.8272 False
75 90 -42958.7548 0.2169 -93805.9709 7888.4614 False
75 120 20334.6842 0.9 -29618.7015 70288.0699 False
75 150 10641.498 0.9 -61918.0966 83201.0925 False
75 160 -40639.0405 0.3293 -92212.9555 10934.8745 False
75 180 -76345.8647 0.0211 -147350.1379 -5341.5915 True
75 190 -49969.6742 0.1395 -105705.162 5765.8136 False
80 85 -23473.1222 0.8357 -64900.7996 17954.5553 False
80 90 -32867.9451 0.0263 -63973.9108 -1761.9793 True
80 120 30425.4939 0.0371 803.1635 60047.8244 True
80 150 20732.3077 0.9 -39658.3909 81123.0063 False
80 160 -30548.2308 0.0875 -62828.417 1731.9554 False
80 180 -66255.0549 0.0103 -124767.8615 -7742.2484 True
80 190 -39878.8645 0.0344 -78461.0665 -1296.6624 True
85 90 -9394.8229 0.9 -49914.2397 31124.594 False
85 120 53898.6161 0.001 14506.6817 93290.5505 True
85 150 44205.4299 0.601 -21531.0023 109941.862 False
85 160 -7075.1086 0.9 -48502.786 34352.5688 False
85 180 -42781.9328 0.6102 -106797.4822 21233.6166 False
85 190 -16405.7423 0.9 -62911.9607 30100.4761 False
90 120 63293.439 0.001 34955.2467 91631.6313 True
90 150 53600.2527 0.1392 -6171.0377 113371.5432 False
90 160 2319.7143 0.9 -28786.2515 33425.68 False
90 180 -33387.1099 0.8128 -91260.413 24486.1932 False
90 190 -7010.9194 0.9 -44616.1983 30594.3595 False
120 150 -9693.1862 0.9 -68705.9689 49319.5964 False
120 160 -60973.7247 0.001 -90596.0551 -31351.3943 True
120 180 -96680.5489 0.001 -153770.1326 -39590.9652 True
120 190 -70304.3584 0.001 -106691.971 -33916.7457 True
150 160 -51280.5385 0.2095 -111671.237 9110.1601 False
150 180 -86987.3626 0.012 -164632.5465 -9342.1788 True
150 190 -60611.1722 0.0871 -124592.5161 3370.1717 False
160 180 -35706.8242 0.7389 -94219.6307 22805.9823 False
160 190 -9330.6337 0.9 -47912.8358 29251.5684 False
180 190 26376.1905 0.9 -35835.741 88588.122 False
-----------------------------------------------------------------
data['Dwell_Type'].replace(40, 20, inplace = True)
data['Dwell_Type'].replace(70, 20, inplace = True)
data['Dwell_Type'].replace(75, 20, inplace = True)
data['Dwell_Type'].replace(80, 20, inplace = True)
data['Dwell_Type'].replace(150, 20, inplace = True)
data['Dwell_Type'].replace(45, 30, inplace = True)
data['Dwell_Type'].replace(180, 30, inplace = True)
data['Dwell_Type'].replace(190, 30, inplace = True)
data['Dwell_Type'].replace(85, 50, inplace = True)
data['Dwell_Type'].replace(90, 50, inplace = True)
data['Dwell_Type'].replace(160, 50, inplace = True)
data["Dwell_Type"].value_counts()
20 917 60 431 50 408 30 203 120 114 Name: Dwell_Type, dtype: int64
data["Dwell_Type"].value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ Dwell_Type', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Dwell_Type 1.0 2.588866e+11 2.588866e+11 59.243897 2.140789e-14 Residual 2071.0 9.049948e+12 4.369844e+09 NaN NaN
tukey_Dwell_Type = pairwise_tukeyhsd(data.Property_Sale_Price, data.Dwell_Type, alpha = 0.05)
print(tukey_Dwell_Type)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
===============================================================
group1 group2 meandiff p-adj lower upper reject
---------------------------------------------------------------
20 30 -55371.9353 0.001 -67812.1704 -42931.7002 True
20 50 -34845.8199 0.001 -44390.15 -25301.4897 True
20 60 48233.2233 0.001 38866.7973 57599.6493 True
20 120 21821.382 0.0018 5893.9961 37748.7679 True
30 50 20526.1155 0.001 6751.0152 34301.2157 True
30 60 103605.1586 0.001 89952.7198 117257.5974 True
30 120 77193.3173 0.001 58422.5749 95964.0598 True
50 60 83079.0431 0.001 72000.9624 94157.1239 True
50 120 56667.2019 0.001 39676.7469 73657.6568 True
60 120 -26411.8413 0.001 -43303.0004 -9520.6821 True
---------------------------------------------------------------
sns.countplot(data.Dwell_Type)
plt.title('Count plot of Dwell_Type')
Text(0.5, 1.0, 'Count plot of Dwell_Type')
sns.catplot(x = data.Dwell_Type , y = data.Property_Sale_Price, data = data)
plt.title('Cat plot of Dwell_Type vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of Dwell_Type vs Property_Sale_Price')
sns.boxplot(x = data.Dwell_Type , y = data.Property_Sale_Price, data = data)
plt.title('Boxplot of Dwell_Type vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot of Dwell_Type vs Property_Sale_Price')
Good Predictor
data.Zone_Class.isnull().sum()
0
data.Zone_Class.value_counts()
RL 1611 RM 324 FV 102 RH 22 C (all) 14 Name: Zone_Class, dtype: int64
data.Zone_Class.value_counts().sum()
2073
sns.countplot(data.Zone_Class)
plt.title('Count plot of Zone_Class')
Text(0.5, 1.0, 'Count plot of Zone_Class')
data.groupby("Zone_Class")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Zone_Class | ||||||||
| C (all) | 14.0 | 70135.857143 | 31906.068856 | 34900.0 | 36483.25 | 74700.0 | 85000.0 | 133900.0 |
| FV | 102.0 | 214299.794118 | 55465.164430 | 130000.0 | 173733.00 | 199900.0 | 250000.0 | 340000.0 |
| RH | 22.0 | 134719.727273 | 31285.377837 | 76000.0 | 119900.00 | 144000.0 | 145000.0 | 200000.0 |
| RL | 1611.0 | 186499.616387 | 65906.057970 | 39300.0 | 138000.00 | 174000.0 | 224250.0 | 340000.0 |
| RM | 324.0 | 129303.089506 | 48130.787063 | 37900.0 | 102750.00 | 122000.0 | 143000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ Zone_Class', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Zone_Class 4.0 1.222868e+12 3.057171e+11 78.187693 7.820516e-62 Residual 2068.0 8.085966e+12 3.910041e+09 NaN NaN
t_Zone_Class = pairwise_tukeyhsd(data.Property_Sale_Price, data.Zone_Class)
print(t_Zone_Class)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
C (all) FV 144163.937 0.001 95506.9159 192820.9581 True
C (all) RH 64583.8701 0.0215 6218.3288 122949.4115 True
C (all) RL 116363.7592 0.001 70539.4929 162188.0256 True
C (all) RM 59167.2324 0.0049 12565.458 105769.0068 True
FV RH -79580.0668 0.001 -119711.0724 -39449.0613 True
FV RL -27800.1777 0.001 -45230.7422 -10369.6133 True
FV RM -84996.7046 0.001 -104379.3404 -65614.0689 True
RH RL 51779.8891 0.0011 15134.8991 88424.8791 True
RH RM -5416.6378 0.9 -43029.3654 32196.0899 False
RL RM -57196.5269 0.001 -67590.9606 -46802.0931 True
-----------------------------------------------------------------
data['Zone_Class'].replace('RH', 'RM', inplace = True)
data.Zone_Class.value_counts()
RL 1611 RM 346 FV 102 C (all) 14 Name: Zone_Class, dtype: int64
data.Zone_Class.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ Zone_Class', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Zone_Class 3.0 1.222264e+12 4.074213e+11 104.241323 7.595204e-63 Residual 2069.0 8.086570e+12 3.908444e+09 NaN NaN
t_Zone_Class = pairwise_tukeyhsd(data.Property_Sale_Price, data.Zone_Class)
print(t_Zone_Class)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
C (all) FV 144163.937 0.001 98350.8216 189977.0523 True
C (all) RL 116363.7592 0.001 73217.8298 159509.6887 True
C (all) RM 59511.6429 0.0028 15691.4668 103331.8189 True
FV RL -27800.1777 0.001 -44211.9605 -11388.395 True
FV RM -84652.2941 0.001 -102762.6009 -66541.9874 True
RL RM -56852.1164 0.001 -66376.4484 -47327.7843 True
-----------------------------------------------------------------
sns.countplot(data.Zone_Class)
plt.title('Count plot of Zone_Class')
Text(0.5, 1.0, 'Count plot of Zone_Class')
sns.catplot('Zone_Class', 'Property_Sale_Price', data = data)
plt.title('Cat plot of Zone_Class vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of Zone_Class vs Property_Sale_Price')
sns.boxplot('Zone_Class', 'Property_Sale_Price', data = data)
plt.title('Boxplot of Zone_Class vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot of Zone_Class vs Property_Sale_Price')
Good Predictor
data.LotFrontage.isnull().sum()
320
data.LotFrontage.dtype
dtype('float64')
data.LotFrontage.sample(10)
951 60.0 1259 65.0 424 72.0 1690 52.0 1146 NaN 1904 78.0 1573 77.0 643 80.0 2065 73.0 1805 76.0 Name: LotFrontage, dtype: float64
data['LotFrontage'] = data["LotFrontage"].fillna(data['LotFrontage'].median())
data.LotFrontage.isnull().sum()
0
data.LotFrontage.describe()
count 2073.000000 mean 71.739026 std 25.746423 min 21.000000 25% 60.000000 50% 70.000000 75% 80.000000 max 313.000000 Name: LotFrontage, dtype: float64
data.LotFrontage.value_counts()
70.0 411
60.0 181
80.0 89
75.0 73
50.0 72
...
138.0 1
137.0 1
150.0 1
112.0 1
46.0 1
Name: LotFrontage, Length: 110, dtype: int64
data.LotFrontage.value_counts().sum()
2073
plt.hist(data.LotFrontage)
plt.title('Hist of LotFrontage')
Text(0.5, 1.0, 'Hist of LotFrontage')
sns.distplot(data.LotFrontage)
plt.title('Normality of LotFrontage')
Text(0.5, 1.0, 'Normality of LotFrontage')
sns.countplot(data.LotFrontage)
plt.title('Count plot of LotFrontage')
Text(0.5, 1.0, 'Count plot of LotFrontage')
sns.boxplot(x = data.LotFrontage, data =data, color = 'r')
plt.title('Box plot of LotFrontage')
Text(0.5, 1.0, 'Box plot of LotFrontage')
IQR = data.LotFrontage.quantile(.75) - data.LotFrontage.quantile(.25)
IQR
20.0
upper_limit = data.LotFrontage.quantile(.75) + (1.5*IQR)
upper_limit
110.0
lower_limit = data.LotFrontage.quantile(.25) - (1.5*IQR)
lower_limit
30.0
len(data.LotFrontage[data.LotFrontage > upper_limit])
68
len(data.LotFrontage[data.LotFrontage < lower_limit])
52
for i in np.arange(upper_limit,200,40):
outliers = len(data.LotFrontage[data.LotFrontage > i])
print('At a limit of :', i, 'There are', outliers, 'outliers')
At a limit of : 110.0 There are 68 outliers At a limit of : 150.0 There are 16 outliers At a limit of : 190.0 There are 9 outliers
for i in np.arange(lower_limit,10,-10):
outliers = len(data.LotFrontage[data.LotFrontage < i])
print('At a limit of :', i, 'There are', outliers, 'outliers')
At a limit of : 30.0 There are 52 outliers At a limit of : 20.0 There are 0 outliers
data['LotFrontage'] = np.where(data['LotFrontage'] > upper_limit, upper_limit, data['LotFrontage'])
data['LotFrontage'] = np.where(data['LotFrontage'] < lower_limit, lower_limit, data['LotFrontage'])
sns.boxplot(x = data.LotFrontage, data =data, color = 'g')
plt.title('Box plot of LotFrontage')
Text(0.5, 1.0, 'Box plot of LotFrontage')
sns.distplot(data.LotFrontage)
plt.title('Dist plot of LotFrontage')
Text(0.5, 1.0, 'Dist plot of LotFrontage')
np.corrcoef(data.LotFrontage, data.Property_Sale_Price)
array([[1. , 0.32062379],
[0.32062379, 1. ]])
sns.scatterplot(x = data['LotFrontage'], y = data.Property_Sale_Price, data = data )
plt.xlabel('LotFrontage')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of LotFrontage vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter plot of LotFrontage vs Property_Sale_Price')
Not good predictor
data.LotArea.isnull().sum()
0
data.LotArea.describe()
count 2073.000000 mean 10717.853353 std 9215.982306 min 1300.000000 25% 7620.000000 50% 9492.000000 75% 11601.000000 max 215245.000000 Name: LotArea, dtype: float64
data.LotArea.value_counts()
7200 28
9600 25
9000 20
6000 19
8400 15
..
7685 1
11777 1
17920 1
14720 1
8190 1
Name: LotArea, Length: 1367, dtype: int64
data.LotArea.value_counts().sum()
2073
plt.hist(data.LotArea)
plt.title('Hist of LotArea')
Text(0.5, 1.0, 'Hist of LotArea')
sns.distplot(data.LotArea)
plt.title('Normality of LotArea')
Text(0.5, 1.0, 'Normality of LotArea')
sns.countplot(data.LotArea)
plt.title('Count plot of LotArea')
Text(0.5, 1.0, 'Count plot of LotArea')
sns.boxplot(x = data.LotArea, data =data, color = 'r')
plt.title('Box plot of LotArea')
Text(0.5, 1.0, 'Box plot of LotArea')
IQR = data.LotArea.quantile(.75) - data.LotArea.quantile(.25)
IQR
3981.0
upper_limit = data.LotArea.quantile(.75) + (1.5*IQR)
upper_limit
17572.5
lower_limit = data.LotArea.quantile(.25) - (1.5*IQR)
lower_limit
1648.5
len(data.LotArea[data.LotArea > upper_limit])
127
len(data.LotArea[data.LotArea < lower_limit])
9
for i in np.arange(upper_limit,50000,10000):
outliers = len(data.LotArea[data.LotArea > i])
print('At a limit of :', i, 'There are', outliers, 'outliers')
At a limit of : 17572.5 There are 127 outliers At a limit of : 27572.5 There are 56 outliers At a limit of : 37572.5 There are 30 outliers At a limit of : 47572.5 There are 11 outliers
for i in np.arange(lower_limit,1300,-1000):
outliers = len(data.LotArea[data.LotArea < i])
print('At a limit of :', i, 'There are', outliers, 'outliers')
At a limit of : 1648.5 There are 9 outliers
data['LotArea'] = np.where(data['LotArea'] > upper_limit, upper_limit, data['LotArea'])
data['LotArea'] = np.where(data['LotArea'] < lower_limit, lower_limit, data['LotArea'])
sns.boxplot(x = data.LotArea, data =data, color = 'g')
plt.title('Box plot of LotArea')
Text(0.5, 1.0, 'Box plot of LotArea')
sns.distplot(data.LotArea)
plt.title('Normality of LotArea')
Text(0.5, 1.0, 'Normality of LotArea')
np.corrcoef(data.LotArea, data.Property_Sale_Price)
array([[1. , 0.35178959],
[0.35178959, 1. ]])
sns.scatterplot(x = data['LotArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('LotArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of LotArea vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter plot of LotArea vs Property_Sale_Price')
Not a good Predictor
data.Road_Type.isnull().sum()
0
data.Road_Type.value_counts()
Pave 2063 Grvl 10 Name: Road_Type, dtype: int64
sns.countplot(data.Road_Type)
plt.title('Count plot of Road_Type')
Text(0.5, 1.0, 'Count plot of Road_Type')
data.groupby('Road_Type')['Property_Sale_Price'].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Road_Type | ||||||||
| Grvl | 10.0 | 119214.300000 | 51482.429909 | 55993.0 | 88250.0 | 110000.0 | 116375.0 | 228950.0 |
| Pave | 2063.0 | 177875.547746 | 66979.790438 | 34900.0 | 130000.0 | 164000.0 | 214000.0 | 340000.0 |
df1 = data[data.Road_Type == 'Pave']
df2 = data[data.Road_Type == 'Grvl']
ttest_ind(df1.Property_Sale_Price, df2.Property_Sale_Price)
Ttest_indResult(statistic=2.765310980668061, pvalue=0.005737285634853254)
Good Predictor
data.Alley.isnull().sum()
1944
data.Alley.value_counts()
Grvl 72 Pave 57 Name: Alley, dtype: int64
data['Alley'] = data['Alley'].fillna('No alley access')
data.Alley.isnull().sum()
0
data.Alley.value_counts()
No alley access 1944 Grvl 72 Pave 57 Name: Alley, dtype: int64
data.Alley.value_counts().sum()
2073
sns.countplot(data.Alley)
plt.title('Count plot of Alley')
Text(0.5, 1.0, 'Count plot of Alley')
data.groupby('Alley')['Property_Sale_Price'].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Alley | ||||||||
| Grvl | 72.0 | 119956.305556 | 34111.276285 | 52500.0 | 102250.0 | 118000.0 | 139550.0 | 256000.0 |
| No alley access | 1944.0 | 179800.450103 | 67683.672106 | 34900.0 | 131000.0 | 165075.0 | 217125.0 | 340000.0 |
| Pave | 57.0 | 175095.947368 | 36880.902953 | 40000.0 | 159434.0 | 174000.0 | 199900.0 | 265979.0 |
mod = ols('Property_Sale_Price ~ Alley', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Alley 2.0 2.490114e+11 1.245057e+11 28.447221 6.490283e-13 Residual 2070.0 9.059823e+12 4.376726e+09 NaN NaN
t_Alley = pairwise_tukeyhsd(data.Property_Sale_Price, data.Alley)
print(t_Alley)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==============================================================================
group1 group2 meandiff p-adj lower upper reject
------------------------------------------------------------------------------
Grvl No alley access 59844.1445 0.001 41222.1851 78466.104 True
Grvl Pave 55139.6418 0.001 27629.9535 82649.3301 True
No alley access Pave -4704.5027 0.8409 -25555.771 16146.7656 False
------------------------------------------------------------------------------
data['Alley'].replace('Pave', 'No alley access', inplace = True)
data.Alley.value_counts()
No alley access 2001 Grvl 72 Name: Alley, dtype: int64
data.Alley.value_counts().sum()
2073
df1=data[data.Alley=='No alley access']
df2=data[data.Alley=='Grvl']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=7.52556317937485, pvalue=7.781545291319476e-14)
sns.countplot(data.Alley)
plt.title('Count plot of Alley')
Text(0.5, 1.0, 'Count plot of Alley')
sns.catplot(x = 'Alley', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Alley vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Alley vs Property_Sale_Price')
sns.boxplot(x = 'Alley', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Alley vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Alley vs Property_Sale_Price')
Good Predictor
data.Property_Shape.isnull().sum()
0
data.Property_Shape.value_counts()
Reg 1300 IR1 698 IR2 60 IR3 15 Name: Property_Shape, dtype: int64
data.Property_Shape.value_counts().sum()
2073
sns.countplot(data.Property_Shape)
plt.title('Count plot of Property_Shape')
Text(0.5, 1.0, 'Count plot of Property_Shape')
data.groupby("Property_Shape")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Property_Shape | ||||||||
| IR1 | 698.0 | 200990.607450 | 67559.667207 | 52000.0 | 148500.0 | 189500.0 | 239900.0 | 340000.0 |
| IR2 | 60.0 | 214172.233333 | 64213.328055 | 110000.0 | 174000.0 | 191250.0 | 248000.0 | 340000.0 |
| IR3 | 15.0 | 203828.333333 | 62233.650511 | 73000.0 | 172250.0 | 192140.0 | 237000.0 | 340000.0 |
| Reg | 1300.0 | 163038.611538 | 62547.325274 | 34900.0 | 120500.0 | 146750.0 | 189000.0 | 340000.0 |
mod= ols("Property_Sale_Price~Property_Shape",data =data).fit()
sm.stats.anova_lm(mod,type = 2)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| Property_Shape | 3.0 | 7.481048e+11 | 2.493683e+11 | 60.268576 | 2.371909e-37 |
| Residual | 2069.0 | 8.560729e+12 | 4.137617e+09 | NaN | NaN |
t_Property_Shape = pairwise_tukeyhsd(data.Property_Sale_Price, data.Property_Shape, alpha = 0.05)
print(t_Property_Shape)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- IR1 IR2 13181.6259 0.4252 -9068.3429 35431.5947 False IR1 IR3 2837.7259 0.9 -40321.0942 45996.546 False IR1 Reg -37951.9959 0.001 -45712.6173 -30191.3746 True IR2 IR3 -10343.9 0.9 -58086.6594 37398.8594 False IR2 Reg -51133.6218 0.001 -72971.9954 -29295.2482 True IR3 Reg -40789.7218 0.0698 -83737.7975 2158.3539 False ---------------------------------------------------------------
data["Property_Shape"].replace("IR3","IR1",inplace = True)
data.Property_Shape.value_counts()
Reg 1300 IR1 713 IR2 60 Name: Property_Shape, dtype: int64
data.Property_Shape.value_counts().sum()
2073
mod= ols("Property_Sale_Price~Property_Shape",data =data).fit()
sm.stats.anova_lm(mod,type = 2)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| Property_Shape | 2.0 | 7.479866e+11 | 3.739933e+11 | 90.431012 | 2.229580e-38 |
| Residual | 2070.0 | 8.560848e+12 | 4.135675e+09 | NaN | NaN |
t_Property_Shape = pairwise_tukeyhsd(data.Property_Sale_Price, data.Property_Shape, alpha = 0.05)
print(t_Property_Shape)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- IR1 IR2 13121.9262 0.2831 -7153.1446 33396.9969 False IR1 Reg -38011.6956 0.001 -45040.7753 -30982.6159 True IR2 Reg -51133.6218 0.001 -71050.2206 -31217.023 True ---------------------------------------------------------------
data["Property_Shape"].replace("IR2","IR1",inplace = True)
data.Property_Shape.value_counts()
Reg 1300 IR1 773 Name: Property_Shape, dtype: int64
data.Property_Shape.value_counts().sum()
2073
df1=data[data.Property_Shape=='Reg']
df2=data[data.Property_Shape=='IR1']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=-13.358349704280727, pvalue=4.188132301676014e-39)
sns.countplot(data.Property_Shape)
plt.title('Count plot of Property_Shape')
Text(0.5, 1.0, 'Count plot of Property_Shape')
sns.catplot(x = 'Property_Shape', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Property_Shape vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Property_Shape vs Property_Sale_Price')
sns.boxplot(x = 'Property_Shape', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Shape vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Property_Shape vs Property_Sale_Price')
Good Predict
data.LandContour.isnull().sum()
0
data.LandContour.value_counts()
Lvl 1854 Bnk 92 HLS 76 Low 51 Name: LandContour, dtype: int64
data.LandContour.value_counts().sum()
2073
sns.countplot(data.LandContour)
plt.title('Count plot of LandContour')
Text(0.5, 1.0, 'Count plot of LandContour')
data.groupby("LandContour")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| LandContour | ||||||||
| Bnk | 92.0 | 141210.728261 | 45566.770113 | 52500.0 | 111000.0 | 134950.0 | 161000.00 | 315000.0 |
| HLS | 76.0 | 214719.315789 | 74459.079139 | 82500.0 | 157250.0 | 201000.0 | 268106.25 | 340000.0 |
| Low | 51.0 | 197807.843137 | 81924.079105 | 39300.0 | 143000.0 | 185000.0 | 267500.00 | 340000.0 |
| Lvl | 1854.0 | 177319.926106 | 66201.326059 | 34900.0 | 130000.0 | 164800.0 | 213187.50 | 340000.0 |
mod= ols("Property_Sale_Price~LandContour",data =data).fit()
aov_table_LandContour = sm.stats.anova_lm(mod)
print(aov_table_LandContour)
df sum_sq mean_sq F PR(>F) LandContour 3.0 2.475121e+11 8.250404e+10 18.838405 4.702250e-12 Residual 2069.0 9.061322e+12 4.379566e+09 NaN NaN
t_LandContour = pairwise_tukeyhsd(data.Property_Sale_Price, data.LandContour, alpha = 0.05)
print(t_LandContour)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- Bnk HLS 73508.5875 0.001 47133.5874 99883.5877 True Bnk Low 56597.1149 0.001 26892.2517 86301.9781 True Bnk Lvl 36109.1978 0.001 17934.752 54283.6437 True HLS Low -16911.4727 0.4924 -47711.3166 13888.3713 False HLS Lvl -37399.3897 0.001 -57313.2535 -17485.5259 True Low Lvl -20487.917 0.1286 -44639.5264 3663.6924 False ---------------------------------------------------------------
data["LandContour"].replace("Low","HLS",inplace = True)
data.LandContour.value_counts()
Lvl 1854 HLS 127 Bnk 92 Name: LandContour, dtype: int64
data.LandContour.value_counts().sum()
2073
mod= ols("Property_Sale_Price~LandContour",data =data).fit()
aov_table_LandContour = sm.stats.anova_lm(mod)
print(aov_table_LandContour)
df sum_sq mean_sq F PR(>F) LandContour 2.0 2.387835e+11 1.193918e+11 27.248025 2.086517e-12 Residual 2070.0 9.070051e+12 4.381667e+09 NaN NaN
t_LandContour = pairwise_tukeyhsd(data.Property_Sale_Price, data.LandContour, alpha = 0.05)
print(t_LandContour)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ============================================================= group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------- Bnk HLS 66717.3662 0.001 45462.1378 87972.5947 True Bnk Lvl 36109.1978 0.001 19526.2348 52692.1609 True HLS Lvl -30608.1684 0.001 -44848.6608 -16367.676 True -------------------------------------------------------------
sns.countplot(data.LandContour)
plt.title('Count plot of LandContour')
Text(0.5, 1.0, 'Count plot of LandContour')
sns.catplot(x = 'LandContour', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Property_Sale_Price vs LandContour')
Text(0.5, 1.0, 'Cat Plot of Property_Sale_Price vs LandContour')
sns.boxplot(x = 'LandContour', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Sale_Price vs LandContour')
Text(0.5, 1.0, 'Box Plot of Property_Sale_Price vs LandContour')
Good Predictor
data.Utilities.isnull().sum()
0
data.Utilities.value_counts()
AllPub 2070 NoSeWa 3 Name: Utilities, dtype: int64
data.Utilities.value_counts().sum()
2073
sns.countplot(data.Utilities)
plt.title('Count plot of Utilities')
Text(0.5, 1.0, 'Count plot of Utilities')
data.groupby("Utilities")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Utilities | ||||||||
| AllPub | 2070.0 | 177650.675362 | 67058.636808 | 34900.0 | 130000.0 | 164000.0 | 214000.0 | 340000.0 |
| NoSeWa | 3.0 | 137500.000000 | 0.000000 | 137500.0 | 137500.0 | 137500.0 | 137500.0 | 137500.0 |
df1=data[data.Utilities=='AllPub']
df2=data[data.Utilities=='NoSeWa']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=1.036797848433364, pvalue=0.29995111758233073)
Not Good Predictor
data.LotConfig.isnull().sum()
0
data.LotConfig.value_counts()
Inside 1481 Corner 386 CulDSac 137 FR2 65 FR3 4 Name: LotConfig, dtype: int64
data.LotConfig.value_counts().sum()
2073
sns.countplot(data.LotConfig)
plt.title('Count plot of LotConfig')
Text(0.5, 1.0, 'Count plot of LotConfig')
data.groupby("LotConfig")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| LotConfig | ||||||||
| Corner | 386.0 | 178871.183938 | 66358.387741 | 52500.0 | 129000.0 | 167250.0 | 221500.0 | 340000.0 |
| CulDSac | 137.0 | 207646.766423 | 64797.175752 | 84000.0 | 149350.0 | 197500.0 | 262500.0 | 340000.0 |
| FR2 | 65.0 | 181016.738462 | 60624.202569 | 81000.0 | 148500.0 | 165000.0 | 214000.0 | 340000.0 |
| FR3 | 4.0 | 208475.000000 | 78379.222374 | 128000.0 | 169925.0 | 195450.0 | 234000.0 | 315000.0 |
| Inside | 1481.0 | 174245.459824 | 66997.868261 | 34900.0 | 128000.0 | 160000.0 | 206900.0 | 340000.0 |
mod = ols("Property_Sale_Price~LotConfig", data =data).fit()
aov_table_LotConfig = sm.stats.anova_lm(mod)
print(aov_table_LotConfig)
df sum_sq mean_sq F PR(>F) LotConfig 4.0 1.455458e+11 3.638646e+10 8.211811 0.000001 Residual 2068.0 9.163288e+12 4.430990e+09 NaN NaN
t_LotConfig = pairwise_tukeyhsd(data.Property_Sale_Price, data.LotConfig, alpha = 0.05)
print(t_LotConfig)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==================================================================
group1 group2 meandiff p-adj lower upper reject
------------------------------------------------------------------
Corner CulDSac 28775.5825 0.001 10702.2906 46848.8744 True
Corner FR2 2145.5545 0.9 -22220.112 26511.221 False
Corner FR3 29603.8161 0.9 -61733.6609 120941.293 False
Corner Inside -4625.7241 0.7153 -15011.5571 5760.1088 False
CulDSac FR2 -26630.028 0.0611 -54001.5582 741.5023 False
CulDSac FR3 828.2336 0.9 -91356.6331 93013.1003 False
CulDSac Inside -33401.3066 0.001 -49630.3081 -17172.3051 True
FR2 FR3 27458.2615 0.9 -66163.8057 121080.3288 False
FR2 Inside -6771.2786 0.9 -29802.1777 16259.6204 False
FR3 Inside -34229.5402 0.8224 -125220.0413 56760.961 False
------------------------------------------------------------------
data["LotConfig"].replace("FR2","Corner",inplace = True)
data["LotConfig"].replace("FR3","Corner",inplace = True)
data.LotConfig.value_counts()
Inside 1481 Corner 455 CulDSac 137 Name: LotConfig, dtype: int64
data.LotConfig.value_counts().sum()
2073
mod = ols("Property_Sale_Price~LotConfig", data =data).fit()
aov_table_LotConfig = sm.stats.anova_lm(mod)
print(aov_table_LotConfig)
df sum_sq mean_sq F PR(>F) LotConfig 2.0 1.418872e+11 7.094361e+10 16.019867 1.247252e-07 Residual 2070.0 9.166947e+12 4.428477e+09 NaN NaN
t_LotConfig = pairwise_tukeyhsd(data.Property_Sale_Price, data.LotConfig, alpha = 0.05)
print(t_LotConfig)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- Corner CulDSac 28208.8214 0.001 12998.3781 43419.2646 True Corner Inside -5192.4852 0.3132 -13558.458 3173.4875 False CulDSac Inside -33401.3066 0.001 -47339.2419 -19463.3713 True -----------------------------------------------------------------
data["LotConfig"].replace("CulDSac","Corner",inplace = True)
data.LotConfig.value_counts()
Inside 1481 Corner 592 Name: LotConfig, dtype: int64
data.LotConfig.value_counts().sum()
2073
df1=data[data.LotConfig=='Inside']
df2=data[data.LotConfig=='Corner']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=-3.6065196077561565, pvalue=0.00031767711757648353)
sns.countplot(data.LotConfig)
plt.title('Count plot of LotConfig')
Text(0.5, 1.0, 'Count plot of LotConfig')
sns.catplot(x = 'LotConfig', y = 'Property_Sale_Price', data = data)
plt.title('LotConfig vs Property_Sale_Price')
Text(0.5, 1.0, 'LotConfig vs Property_Sale_Price')
sns.boxplot(x = 'LotConfig', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of LotConfig vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of LotConfig vs Property_Sale_Price')
Good Predictor
data.LandSlope.isnull().sum()
0
data.LandSlope.value_counts()
Gtl 1958 Mod 101 Sev 14 Name: LandSlope, dtype: int64
data.LandSlope.value_counts().sum()
2073
sns.countplot(data.LandSlope)
plt.title('Count Plot of LandSlope')
Text(0.5, 1.0, 'Count Plot of LandSlope')
data.groupby("LandSlope")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| LandSlope | ||||||||
| Gtl | 1958.0 | 176946.424413 | 66471.654792 | 34900.0 | 130000.0 | 163000.0 | 213000.0 | 340000.0 |
| Mod | 101.0 | 187191.772277 | 75128.837126 | 39300.0 | 130000.0 | 172000.0 | 242000.0 | 340000.0 |
| Sev | 14.0 | 198709.285714 | 78967.344611 | 61000.0 | 147250.0 | 182500.0 | 255375.0 | 340000.0 |
mod = ols("Property_Sale_Price~LandSlope",data =data).fit()
aov_table_LandSlope = sm.stats.anova_lm(mod)
print(aov_table_LandSlope)
df sum_sq mean_sq F PR(>F) LandSlope 2.0 1.636691e+10 8.183453e+09 1.822955 0.161807 Residual 2070.0 9.292467e+12 4.489115e+09 NaN NaN
Not Good Predictor
data.Neighborhood.isnull().sum()
0
data.Neighborhood.value_counts()
NAmes 317 CollgCr 217 OldTown 156 Edwards 138 Somerst 123 Gilbert 119 Sawyer 108 NWAmes 105 NridgHt 99 SawyerW 93 BrkSide 80 Mitchel 78 Crawfor 72 NoRidge 65 Timber 53 IDOTRR 51 ClearCr 33 StoneBr 32 SWISU 31 Blmngtn 27 MeadowV 24 BrDale 17 NPkVill 17 Veenker 15 Blueste 3 Name: Neighborhood, dtype: int64
data.Neighborhood.value_counts().sum()
2073
sns.countplot(data.Neighborhood)
plt.title('Count plot of Neighborhood')
Text(0.5, 1.0, 'Count plot of Neighborhood')
data.groupby("Neighborhood")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Neighborhood | ||||||||
| Blmngtn | 27.0 | 190835.666667 | 29284.016787 | 159895.0 | 173250.0 | 181000.0 | 203845.5 | 264561.0 |
| Blueste | 3.0 | 133000.000000 | 15588.457268 | 124000.0 | 124000.0 | 124000.0 | 137500.0 | 151000.0 |
| BrDale | 17.0 | 105288.235294 | 14256.572272 | 83000.0 | 91500.0 | 106000.0 | 118000.0 | 125000.0 |
| BrkSide | 80.0 | 125134.687500 | 42537.203280 | 39300.0 | 88750.0 | 120000.0 | 142125.0 | 223500.0 |
| ClearCr | 33.0 | 216852.484848 | 51130.435689 | 130000.0 | 187500.0 | 200500.0 | 244400.0 | 328000.0 |
| CollgCr | 217.0 | 198702.612903 | 49612.254693 | 110000.0 | 150500.0 | 200000.0 | 230000.0 | 340000.0 |
| Crawfor | 72.0 | 210319.597222 | 62955.783040 | 90350.0 | 160000.0 | 217000.0 | 250000.0 | 340000.0 |
| Edwards | 138.0 | 130126.014493 | 44063.138003 | 58500.0 | 103925.0 | 125000.0 | 145900.0 | 320000.0 |
| Gilbert | 119.0 | 190821.268908 | 29959.671152 | 141000.0 | 174500.0 | 180500.0 | 193000.0 | 340000.0 |
| IDOTRR | 51.0 | 99941.215686 | 33470.887694 | 34900.0 | 82750.0 | 106500.0 | 120000.0 | 169500.0 |
| MeadowV | 24.0 | 96362.500000 | 22172.423383 | 75000.0 | 80750.0 | 88000.0 | 101500.0 | 151400.0 |
| Mitchel | 78.0 | 155801.846154 | 37567.390308 | 84500.0 | 131000.0 | 152200.0 | 169425.0 | 271000.0 |
| NAmes | 317.0 | 144819.851735 | 32532.996994 | 87500.0 | 126500.0 | 140000.0 | 156500.0 | 340000.0 |
| NPkVill | 17.0 | 144220.588235 | 9197.436104 | 127500.0 | 143750.0 | 143750.0 | 148500.0 | 155000.0 |
| NWAmes | 105.0 | 191622.476190 | 36395.292451 | 82500.0 | 173000.0 | 185000.0 | 207500.0 | 299800.0 |
| NoRidge | 65.0 | 296817.446154 | 41736.133024 | 190000.0 | 268000.0 | 302000.0 | 340000.0 | 340000.0 |
| NridgHt | 99.0 | 289969.454545 | 57226.679072 | 154000.0 | 254750.0 | 318000.0 | 340000.0 | 340000.0 |
| OldTown | 156.0 | 122497.173077 | 42776.065932 | 37900.0 | 104750.0 | 118250.0 | 135000.0 | 340000.0 |
| SWISU | 31.0 | 143701.096774 | 32696.637808 | 60000.0 | 127500.0 | 139500.0 | 164500.0 | 200000.0 |
| Sawyer | 108.0 | 141572.879630 | 30697.874546 | 62383.0 | 127750.0 | 135000.0 | 151750.0 | 310000.0 |
| SawyerW | 93.0 | 185179.118280 | 54575.658327 | 76000.0 | 144000.0 | 179200.0 | 225000.0 | 320000.0 |
| Somerst | 123.0 | 224583.373984 | 51250.299541 | 144152.0 | 179400.0 | 225000.0 | 253000.0 | 340000.0 |
| StoneBr | 32.0 | 270671.875000 | 63086.296423 | 170000.0 | 213125.0 | 275000.0 | 340000.0 | 340000.0 |
| Timber | 53.0 | 234707.415094 | 58939.456608 | 137500.0 | 187500.0 | 224500.0 | 283463.0 | 340000.0 |
| Veenker | 15.0 | 258100.000000 | 69359.621848 | 162500.0 | 190500.0 | 270000.0 | 332000.0 | 340000.0 |
mod = ols("Property_Sale_Price~Neighborhood",data =data).fit()
aov_table_Neighborhood = sm.stats.anova_lm(mod)
print(aov_table_Neighborhood)
df sum_sq mean_sq F PR(>F) Neighborhood 24.0 5.406891e+12 2.252871e+11 118.245708 0.0 Residual 2048.0 3.901943e+12 1.905246e+09 NaN NaN
t_Neighborhood = pairwise_tukeyhsd(data.Property_Sale_Price, data.Neighborhood, alpha = 0.05)
print(t_Neighborhood)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ==================================================================== group1 group2 meandiff p-adj lower upper reject -------------------------------------------------------------------- Blmngtn Blueste -57835.6667 0.8804 -155142.6019 39471.2686 False Blmngtn BrDale -85547.4314 0.001 -135052.0446 -36042.8181 True Blmngtn BrkSide -65700.9792 0.001 -101287.9227 -30114.0356 True Blmngtn ClearCr 26016.8182 0.8004 -15474.9984 67508.6348 False Blmngtn CollgCr 7866.9462 0.9 -24762.4352 40496.3276 False Blmngtn Crawfor 19483.9306 0.9 -16598.4467 55566.3078 False Blmngtn Edwards -60709.6522 0.001 -94356.6425 -27062.6619 True Blmngtn Gilbert -14.3978 0.9 -34098.0983 34069.3028 False Blmngtn IDOTRR -90894.451 0.001 -128948.9499 -52839.9521 True Blmngtn MeadowV -94473.1667 0.001 -139329.4474 -49616.8859 True Blmngtn Mitchel -35033.8205 0.0629 -70735.7052 668.0641 False Blmngtn NAmes -46015.8149 0.001 -78070.638 -13960.9918 True Blmngtn NPkVill -46615.0784 0.0979 -96119.6917 2889.5348 False Blmngtn NWAmes 786.8095 0.9 -33714.5422 35288.1612 False Blmngtn NoRidge 105981.7795 0.001 69373.3558 142590.2032 True Blmngtn NridgHt 99133.7879 0.001 64419.2435 133848.3323 True Blmngtn OldTown -68338.4936 0.001 -101666.3238 -35010.6634 True Blmngtn SWISU -47134.5699 0.0101 -89224.3778 -5044.7619 True Blmngtn Sawyer -49262.787 0.001 -83665.9839 -14859.5902 True Blmngtn SawyerW -5656.5484 0.9 -40610.2314 29297.1346 False Blmngtn Somerst 33747.7073 0.0544 -233.3485 67728.7631 False Blmngtn StoneBr 79836.2083 0.001 38053.6709 121618.7458 True Blmngtn Timber 43871.7484 0.0055 6066.6082 81676.8886 True Blmngtn Veenker 67264.3333 0.001 15774.343 118754.3236 True Blueste BrDale -27711.7647 0.9 -127839.775 72416.2456 False Blueste BrkSide -7865.3125 0.9 -101893.7245 86163.0995 False Blueste ClearCr 83852.4848 0.202 -12565.784 180270.7537 False Blueste CollgCr 65702.6129 0.6049 -27246.7725 158651.9983 False Blueste Crawfor 77319.5972 0.3071 -16897.4377 171536.6321 False Blueste Edwards -2873.9855 0.9 -96185.4621 90437.4911 False Blueste Gilbert 57821.2689 0.821 -35648.5677 151291.1055 False Blueste IDOTRR -33058.7843 0.9 -128048.5554 61930.9867 False Blueste MeadowV -36637.5 0.9 -134550.7148 61275.7148 False Blueste Mitchel 22801.8462 0.9 -71270.1278 116873.8201 False Blueste NAmes 11819.8517 0.9 -80929.3984 104569.1019 False Blueste NPkVill 11220.5882 0.9 -88907.4221 111348.5985 False Blueste NWAmes 58622.4762 0.8026 -35000.4643 152245.4167 False Blueste NoRidge 163817.4462 0.001 69397.7003 258237.192 True Blueste NridgHt 156969.4545 0.001 63267.74 250671.1691 True Blueste OldTown -10502.8269 0.9 -103699.6937 82694.0399 False Blueste SWISU 10701.0968 0.9 -85976.0135 107378.207 False Blueste Sawyer 8572.8796 0.9 -85013.9339 102159.6932 False Blueste SawyerW 52179.1183 0.9 -41611.4552 145969.6917 False Blueste Somerst 91583.374 0.0638 -1849.0822 185015.8302 False Blueste StoneBr 137671.875 0.001 41128.1432 234215.6068 True Blueste Timber 101707.4151 0.0198 6817.2663 196597.5639 True Blueste Veenker 125100.0 0.0016 23975.6666 226224.3334 True BrDale BrkSide 19846.4522 0.9 -22854.9454 62547.8498 False BrDale ClearCr 111564.2496 0.001 63830.0936 159298.4055 True BrDale CollgCr 93414.3776 0.001 53144.5957 133684.1595 True BrDale Crawfor 105031.3619 0.001 61916.205 148146.5188 True BrDale Edwards 24837.7792 0.856 -16260.8675 65936.4259 False BrDale Gilbert 85533.0336 0.001 44076.0988 126989.9685 True BrDale IDOTRR -5347.0196 0.9 -50125.6271 39431.5879 False BrDale MeadowV -8925.7353 0.9 -59611.6774 41760.2068 False BrDale Mitchel 50513.6109 0.0041 7716.3753 93310.8464 True BrDale NAmes 39531.6164 0.0545 -274.0425 79337.2754 False BrDale NPkVill 38932.3529 0.5974 -15910.0169 93774.7228 False BrDale NWAmes 86334.2409 0.001 44533.2592 128135.2226 True BrDale NoRidge 191529.2109 0.001 147972.8624 235085.5593 True BrDale NridgHt 184681.2193 0.001 142704.1018 226658.3368 True BrDale OldTown 17208.9378 0.9 -23628.8274 58046.703 False BrDale SWISU 38412.8615 0.3722 -9841.9905 86667.7134 False BrDale Sawyer 36284.6443 0.2019 -5435.36 78004.6486 False BrDale SawyerW 79890.883 0.001 37715.7866 122065.9794 True BrDale Somerst 119295.1387 0.001 77922.5517 160667.7257 True BrDale StoneBr 165383.6397 0.001 117396.5661 213370.7134 True BrDale Timber 129419.1798 0.001 84852.2927 173986.0669 True BrDale Veenker 152811.7647 0.001 96170.8086 209452.7208 True BrkSide ClearCr 91717.7973 0.001 58637.9928 124797.6019 True BrkSide CollgCr 73567.9254 0.001 52654.3137 94481.5371 True BrkSide Crawfor 85184.9097 0.001 59211.0657 111158.7538 True BrkSide Edwards 4991.327 0.9 -17476.9302 27459.5842 False BrkSide Gilbert 65686.5814 0.001 42569.4607 88803.7021 True BrkSide IDOTRR -25193.4718 0.1843 -53843.9083 3456.9647 False BrkSide MeadowV -28772.1875 0.4377 -65984.9346 8440.5596 False BrkSide Mitchel 30667.1587 0.0027 5224.5332 56109.7841 True BrkSide NAmes 19685.1642 0.0609 -320.1927 39690.5212 False BrkSide NPkVill 19085.9007 0.9 -23615.4968 61787.2983 False BrkSide NWAmes 66487.7887 0.001 42759.2006 90216.3768 True BrkSide NoRidge 171682.7587 0.001 144982.9588 198382.5585 True BrkSide NridgHt 164834.767 0.001 140797.2502 188872.2839 True BrkSide OldTown -2637.5144 0.9 -24624.9401 19349.9113 False BrkSide SWISU 18566.4093 0.9 -15260.4226 52393.2412 False BrkSide Sawyer 16438.1921 0.6291 -7147.4514 40023.8356 False BrkSide SawyerW 60044.4308 0.001 35662.8275 84426.034 True BrkSide Somerst 99448.6865 0.001 76483.1737 122414.1992 True BrkSide StoneBr 145537.1875 0.001 112093.4578 178980.9172 True BrkSide Timber 109572.7276 0.001 81254.3369 137891.1182 True BrkSide Veenker 132965.3125 0.001 87977.2949 177953.3301 True ClearCr CollgCr -18149.8719 0.8482 -48024.9359 11725.192 False ClearCr Crawfor -6532.8876 0.9 -40145.1007 27079.3254 False ClearCr Edwards -86726.4704 0.001 -117709.738 -55743.2027 True ClearCr Gilbert -26031.2159 0.2909 -57488.1955 5425.7636 False ClearCr IDOTRR -116911.2692 0.001 -152632.2406 -81190.2977 True ClearCr MeadowV -120489.9848 0.001 -163384.3766 -77595.5931 True ClearCr Mitchel -61050.6387 0.001 -94254.0645 -27847.2129 True ClearCr NAmes -72032.6331 0.001 -101279.079 -42786.1872 True ClearCr NPkVill -72631.8966 0.001 -120366.0526 -24897.7406 True ClearCr NWAmes -25230.0087 0.3875 -57139.0385 6679.0212 False ClearCr NoRidge 79964.9613 0.001 45788.6595 114141.2631 True ClearCr NridgHt 73116.9697 0.001 40977.5468 105256.3926 True ClearCr OldTown -94355.3118 0.001 -124991.682 -63718.9415 True ClearCr SWISU -73151.3881 0.001 -113143.823 -33158.9532 True ClearCr Sawyer -75279.6052 0.001 -107082.4804 -43476.73 True ClearCr SawyerW -31673.3666 0.0658 -64070.9416 724.2085 False ClearCr Somerst 7730.8891 0.9 -23614.8453 39076.6236 False ClearCr StoneBr 53819.3902 0.001 14150.4684 93488.3119 True ClearCr Timber 17854.9302 0.9 -17600.2745 53310.135 False ClearCr Veenker 41247.5152 0.2888 -8542.6648 91037.6951 False CollgCr Crawfor 11616.9843 0.9 -10128.9706 33362.9392 False CollgCr Edwards -68576.5984 0.001 -85985.4628 -51167.734 True CollgCr Gilbert -7881.344 0.9 -26119.9637 10357.2757 False CollgCr IDOTRR -98761.3972 0.001 -123642.9866 -73879.8079 True CollgCr MeadowV -102340.1129 0.001 -136735.3819 -67944.8439 True CollgCr Mitchel -42900.7667 0.001 -64009.3709 -21792.1626 True CollgCr NAmes -53882.7612 0.001 -67970.3545 -39795.1678 True CollgCr NPkVill -54482.0247 0.001 -94751.8066 -14212.2428 True CollgCr NWAmes -7080.1367 0.9 -26087.816 11927.5426 False CollgCr NoRidge 98114.8333 0.001 75506.753 120722.9135 True CollgCr NridgHt 91266.8416 0.001 71874.8796 110658.8037 True CollgCr OldTown -76205.4398 0.001 -92989.1483 -59421.7314 True CollgCr SWISU -55001.5161 0.001 -85701.6877 -24301.3445 True CollgCr Sawyer -57129.7333 0.001 -75958.662 -38300.8045 True CollgCr SawyerW -13523.4946 0.6647 -33340.3702 6293.3809 False CollgCr Somerst 25880.7611 0.001 7834.688 43926.8342 True CollgCr StoneBr 71969.2621 0.001 41691.7286 102246.7956 True CollgCr Timber 36004.8022 0.001 11506.2874 60503.317 True CollgCr Veenker 59397.3871 0.001 16710.5308 102084.2434 True Crawfor Edwards -80193.5827 0.001 -103438.5818 -56948.5837 True Crawfor Gilbert -19498.3283 0.3164 -43371.0883 4374.4316 False Crawfor IDOTRR -110378.3815 0.001 -139641.9239 -81114.8392 True Crawfor MeadowV -113957.0972 0.001 -151643.9112 -76270.2833 True Crawfor Mitchel -54517.7511 0.001 -80648.8549 -28386.6472 True Crawfor NAmes -65499.7455 0.001 -86373.6935 -44625.7975 True Crawfor NPkVill -66099.009 0.001 -109214.1659 -22983.8521 True Crawfor NWAmes -18697.121 0.4624 -43162.4698 5768.2278 False Crawfor NoRidge 86497.8489 0.001 59141.1914 113854.5064 True Crawfor NridgHt 79649.8573 0.001 54884.7687 104414.9459 True Crawfor OldTown -87822.4241 0.001 -110602.9924 -65041.8559 True Crawfor SWISU -66618.5004 0.001 -100966.1635 -32270.8374 True Crawfor Sawyer -68746.7176 0.001 -93073.4514 -44419.9838 True Crawfor SawyerW -25140.4789 0.049 -50239.6816 -41.2762 True Crawfor Somerst 14263.7768 0.8637 -9462.2042 37989.7578 False Crawfor StoneBr 60352.2778 0.001 26381.8427 94322.7128 True Crawfor Timber 24387.8179 0.2561 -4550.7144 53326.3501 False Crawfor Veenker 47780.4028 0.0255 2399.4693 93161.3362 True Edwards Gilbert 60695.2544 0.001 40692.9877 80697.5211 True Edwards IDOTRR -30184.7988 0.0063 -56386.6483 -3982.9493 True Edwards MeadowV -33763.5145 0.0855 -69125.6122 1598.5832 False Edwards Mitchel 25675.8317 0.0083 3025.962 48325.7013 True Edwards NAmes 14693.8372 0.1496 -1612.717 31000.3915 False Edwards NPkVill 14094.5737 0.9 -27004.073 55193.2204 False Edwards NWAmes 61496.4617 0.001 40790.5375 82202.3859 True Edwards NoRidge 166691.4317 0.001 142637.9763 190744.8871 True Edwards NridgHt 159843.4401 0.001 138784.1993 180902.6808 True Edwards OldTown -7628.8414 0.9 -26314.0306 11056.3478 False Edwards SWISU 13575.0823 0.9 -18204.5332 45354.6978 False Edwards Sawyer 11446.8651 0.9 -9095.0915 31988.8218 False Edwards SawyerW 55053.1038 0.001 33601.9504 76504.2572 True Edwards Somerst 94457.3595 0.001 74630.5045 114284.2145 True Edwards StoneBr 140545.8605 0.001 109174.3374 171917.3836 True Edwards Timber 104581.4006 0.001 78743.0443 130419.7569 True Edwards Veenker 127973.9855 0.001 84504.3279 171443.6431 True Gilbert IDOTRR -90880.0532 0.001 -117640.3896 -64119.7168 True Gilbert MeadowV -94458.7689 0.001 -130236.6481 -58680.8897 True Gilbert Mitchel -35019.4228 0.001 -58313.0975 -11725.748 True Gilbert NAmes -46001.4172 0.001 -63191.0187 -28811.8157 True Gilbert NPkVill -46600.6807 0.0095 -88057.6155 -5143.7458 True Gilbert NWAmes 801.2073 0.9 -20607.0618 22209.4764 False Gilbert NoRidge 105996.1772 0.001 81335.5324 130656.8221 True Gilbert NridgHt 99148.1856 0.001 77398.0062 120898.3651 True Gilbert OldTown -68324.0958 0.001 -87784.6966 -48863.4951 True Gilbert SWISU -47120.1721 0.001 -79361.8013 -14878.543 True Gilbert Sawyer -49248.3893 0.001 -70498.111 -27998.6676 True Gilbert SawyerW -5642.1506 0.9 -27772.0099 16487.7086 False Gilbert Somerst 33762.1051 0.001 13202.8525 54321.3577 True Gilbert StoneBr 79850.6061 0.001 48011.1471 111690.0651 True Gilbert Timber 43886.1462 0.001 17481.6136 70290.6788 True Gilbert Veenker 67278.7311 0.001 23470.1724 111087.2898 True IDOTRR MeadowV -3578.7157 0.9 -43157.787 36000.3556 False IDOTRR Mitchel 55860.6305 0.001 27067.5493 84653.7117 True IDOTRR NAmes 44878.636 0.001 20755.4389 69001.8332 True IDOTRR NPkVill 44279.3725 0.0574 -499.2349 89057.98 False IDOTRR NWAmes 91681.2605 0.001 64390.965 118971.556 True IDOTRR NoRidge 196876.2305 0.001 166966.4721 226785.9888 True IDOTRR NridgHt 190028.2389 0.001 162468.9111 217587.5667 True IDOTRR OldTown 22555.9574 0.1929 -3234.7623 48346.6771 False IDOTRR SWISU 43759.8811 0.0029 7346.0253 80173.7368 True IDOTRR Sawyer 41631.6639 0.001 14465.5653 68797.7625 True IDOTRR SawyerW 85237.9026 0.001 57377.9508 113097.8544 True IDOTRR Somerst 124642.1583 0.001 98012.6801 151271.6365 True IDOTRR StoneBr 170730.6593 0.001 134672.4093 206788.9093 True IDOTRR Timber 134766.1994 0.001 103403.0748 166129.324 True IDOTRR Veenker 158158.7843 0.001 111194.5846 205122.9841 True MeadowV Mitchel 59439.3462 0.001 22116.6645 96762.0278 True MeadowV NAmes 48457.3517 0.001 14606.6549 82308.0486 True MeadowV NPkVill 47858.0882 0.0951 -2827.8539 98544.0303 False MeadowV NWAmes 95259.9762 0.001 59083.9998 131435.9525 True MeadowV NoRidge 200454.9462 0.001 162264.1792 238645.7131 True MeadowV NridgHt 193606.9545 0.001 157227.5979 229986.3111 True MeadowV OldTown 26134.6731 0.5103 -8923.8817 61193.2278 False MeadowV SWISU 47338.5968 0.0157 3865.5025 90811.6911 True MeadowV Sawyer 45210.3796 0.0013 9128.0023 81292.7569 True MeadowV SawyerW 88816.6183 0.001 52208.9968 125424.2397 True MeadowV Somerst 128220.874 0.001 92540.7654 163900.9825 True MeadowV StoneBr 174309.375 0.001 131133.7056 217485.0444 True MeadowV Timber 138344.9151 0.001 99005.5376 177684.2926 True MeadowV Veenker 161737.5 0.001 109110.7283 214364.2717 True Mitchel NAmes -10981.9944 0.9 -31191.1093 9227.1204 False Mitchel NPkVill -11581.2579 0.9 -54378.4935 31215.9776 False Mitchel NWAmes 35820.63 0.001 11920.0044 59721.2557 True Mitchel NoRidge 141015.6 0.001 114162.7915 167868.4085 True Mitchel NridgHt 134167.6084 0.001 109960.2494 158374.9674 True Mitchel OldTown -33304.6731 0.001 -55477.65 -11131.6962 True Mitchel SWISU -12100.7494 0.9 -46048.4823 21846.9836 False Mitchel Sawyer -14228.9665 0.8693 -37987.6827 9529.7496 False Mitchel SawyerW 29377.2721 0.0031 4828.2071 53926.3371 True Mitchel Somerst 68781.5278 0.001 45638.3044 91924.7513 True Mitchel StoneBr 114870.0288 0.001 81304.0182 148436.0395 True Mitchel Timber 78905.5689 0.001 50442.8694 107368.2685 True Mitchel Veenker 102298.1538 0.001 57219.1594 147377.1483 True NAmes NPkVill -599.2635 0.9 -40404.9225 39206.3955 False NAmes NWAmes 46802.6245 0.001 28799.0963 64806.1526 True NAmes NoRidge 151997.5944 0.001 130226.9618 173768.227 True NAmes NridgHt 145149.6028 0.001 126740.8183 163558.3873 True NAmes OldTown -22322.6787 0.001 -37960.0704 -6685.2869 True NAmes SWISU -1118.755 0.9 -31207.5517 28970.0418 False NAmes Sawyer -3246.9721 0.9 -21061.677 14567.7328 False NAmes SawyerW 40359.2665 0.001 21503.3998 59215.1332 True NAmes Somerst 79763.5222 0.001 62778.355 96748.6895 True NAmes StoneBr 125852.0233 0.001 96194.5758 155509.4707 True NAmes Timber 89887.5634 0.001 66159.6814 113615.4453 True NAmes Veenker 113280.1483 0.001 71030.8541 155529.4424 True NPkVill NWAmes 47401.888 0.0082 5600.9062 89202.8697 True NPkVill NoRidge 152596.8579 0.001 109040.5095 196153.2064 True NPkVill NridgHt 145748.8663 0.001 103771.7488 187725.9838 True NPkVill OldTown -21723.4152 0.9 -62561.1804 19114.3501 False NPkVill SWISU -519.4915 0.9 -48774.3434 47735.3605 False NPkVill Sawyer -2647.7086 0.9 -44367.7129 39072.2957 False NPkVill SawyerW 40958.53 0.071 -1216.5664 83133.6264 False NPkVill Somerst 80362.7857 0.001 38990.1988 121735.3727 True NPkVill StoneBr 126451.2868 0.001 78464.2131 174438.3604 True NPkVill Timber 90486.8269 0.001 45919.9397 135053.714 True NPkVill Veenker 113879.4118 0.001 57238.4557 170520.3678 True NWAmes NoRidge 105194.97 0.001 79960.2314 130429.7085 True NWAmes NridgHt 98346.9784 0.001 75947.9844 120745.9723 True NWAmes OldTown -69125.3031 0.001 -89308.4547 -48942.1516 True NWAmes SWISU -47921.3794 0.001 -80604.2078 -15238.551 True NWAmes Sawyer -50049.5966 0.001 -71962.9554 -28136.2377 True NWAmes SawyerW -6443.3579 0.9 -29211.2145 16324.4987 False NWAmes Somerst 32960.8978 0.001 11716.4281 54205.3675 True NWAmes StoneBr 79049.3988 0.001 46763.2443 111335.5534 True NWAmes Timber 43084.9389 0.001 16143.4474 70026.4304 True NWAmes Veenker 66477.5238 0.001 22343.2464 110611.8012 True NoRidge NridgHt -6847.9916 0.9 -32373.4368 18677.4535 False NoRidge OldTown -174320.2731 0.001 -197925.2095 -150715.3366 True NoRidge SWISU -153116.3494 0.001 -188016.216 -118216.4827 True NoRidge Sawyer -155244.5665 0.001 -180344.9393 -130144.1937 True NoRidge SawyerW -111638.3279 0.001 -137488.0612 -85788.5945 True NoRidge Somerst -72234.0722 0.001 -96752.6551 -47715.4892 True NoRidge StoneBr -26145.5712 0.481 -60674.2432 8383.1009 False NoRidge Timber -62110.0311 0.001 -91701.8777 -32518.1845 True NoRidge Veenker -38717.4462 0.2504 -84517.7503 7082.858 False NridgHt OldTown -167472.2815 0.001 -188017.7415 -146926.8214 True NridgHt SWISU -146268.3578 0.001 -179176.1623 -113360.5533 True NridgHt Sawyer -148396.5749 0.001 -170644.083 -126149.0668 True NridgHt SawyerW -104790.3363 0.001 -127879.9793 -81700.6932 True NridgHt Somerst -65386.0806 0.001 -86975.0549 -43797.1062 True NridgHt StoneBr -19297.5795 0.8825 -51811.455 13216.2959 False NridgHt Timber -55262.0395 0.001 -82476.0117 -28048.0672 True NridgHt Veenker -31869.4545 0.5742 -76170.5918 12431.6827 False OldTown SWISU 21203.9237 0.6844 -10237.5818 52645.4292 False OldTown Sawyer 19075.7066 0.0871 -939.1952 39090.6083 False OldTown SawyerW 62681.9452 0.001 41734.9576 83628.9328 True OldTown Somerst 102086.2009 0.001 82805.9393 121366.4625 True OldTown StoneBr 148174.7019 0.001 117145.7353 179203.6686 True OldTown Timber 112210.242 0.001 86788.8935 137631.5906 True OldTown Veenker 135602.8269 0.001 92379.7378 178825.9161 True SWISU Sawyer -2128.2171 0.9 -34707.4123 30450.9781 False SWISU SawyerW 41478.0215 0.0013 8318.0463 74637.9968 True SWISU Somerst 80882.2772 0.001 48749.1766 113015.3778 True SWISU StoneBr 126970.7782 0.001 86676.803 167264.7534 True SWISU Timber 91006.3183 0.001 54853.1355 127159.5012 True SWISU Veenker 114398.9032 0.001 64109.3108 164688.4956 True Sawyer SawyerW 43606.2386 0.001 20987.3974 66225.0799 True Sawyer Somerst 83010.4944 0.001 61925.8037 104095.185 True Sawyer StoneBr 129098.9954 0.001 96917.7514 161280.2393 True Sawyer Timber 93134.5355 0.001 66318.8562 119950.2147 True Sawyer Veenker 116527.1204 0.001 72469.5316 160584.7091 True SawyerW Somerst 39404.2557 0.001 17432.8157 61375.6957 True SawyerW StoneBr 85492.7567 0.001 52723.6789 118261.8345 True SawyerW Timber 49528.2968 0.001 22009.9275 77046.6661 True SawyerW Veenker 72920.8817 0.001 28432.1064 117409.657 True Somerst StoneBr 46088.501 0.001 14358.9461 77818.056 True Somerst Timber 10124.0411 0.9 -16147.8612 36395.9434 False Somerst Veenker 33516.626 0.4563 -10212.1211 77245.3731 False StoneBr Timber -35964.4599 0.0473 -71759.4474 -169.4724 True StoneBr Veenker -12571.875 0.9 -62604.5804 37460.8304 False Timber Veenker 23392.5849 0.9 -23369.791 70154.9608 False --------------------------------------------------------------------
data["Neighborhood"].replace("Blueste","Blmngtn",inplace = True)
data["Neighborhood"].replace("Blmngtn","CollgCr",inplace = True)
data["Neighborhood"].replace("BrDale","BrkSide",inplace = True)
data["Neighborhood"].replace("BrkSide","Edwards",inplace = True)
data["Neighborhood"].replace("ClearCr","Crawfor",inplace = True)
data["Neighborhood"].replace("Gilbert","CollgCr",inplace = True)
data["Neighborhood"].replace("NWAmes","CollgCr",inplace = True)
data["Neighborhood"].replace("SawyerW","CollgCr",inplace = True)
data["Neighborhood"].replace("NPkVill","Edwards",inplace = True)
data["Neighborhood"].replace("OldTown","Edwards",inplace = True)
data["Neighborhood"].replace("MeadowV","IDOTRR",inplace = True)
data["Neighborhood"].replace("SWISU","Mitchel",inplace = True)
data["Neighborhood"].replace("Mitchel","NAmes",inplace = True)
data["Neighborhood"].replace("Sawyer","NAmes",inplace = True)
data["Neighborhood"].replace("NoRidge","NridgHt",inplace = True)
data["Neighborhood"].replace("Timber","Somerst",inplace = True)
data["Neighborhood"].replace("Veenker","StoneBr",inplace = True)
data["Neighborhood"].replace("Crawfor","Somerst",inplace = True)
data.Neighborhood.value_counts()
CollgCr 564 NAmes 534 Edwards 408 Somerst 281 NridgHt 164 IDOTRR 75 StoneBr 47 Name: Neighborhood, dtype: int64
mod = ols("Property_Sale_Price~Neighborhood",data =data).fit()
aov_table_Neighborhood = sm.stats.anova_lm(mod)
print(aov_table_Neighborhood)
df sum_sq mean_sq F PR(>F) Neighborhood 6.0 5.331338e+12 8.885564e+11 461.536006 0.0 Residual 2066.0 3.977496e+12 1.925216e+09 NaN NaN
t_Neighborhood = pairwise_tukeyhsd(data.Property_Sale_Price, data.Neighborhood, alpha = 0.05)
print(t_Neighborhood)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================== group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------------ CollgCr Edwards -66982.7862 0.001 -75398.7161 -58566.8563 True CollgCr IDOTRR -93969.5389 0.001 -109884.9865 -78054.0914 True CollgCr NAmes -47063.2398 0.001 -54881.8507 -39244.6289 True CollgCr NridgHt 99918.032 0.001 88430.0743 111405.9896 True CollgCr Somerst 29164.6515 0.001 19709.3745 38619.9284 True CollgCr StoneBr 73894.0089 0.001 54234.5873 93553.4304 True Edwards IDOTRR -26986.7527 0.001 -43255.4008 -10718.1047 True Edwards NAmes 19919.5464 0.001 11404.964 28434.1288 True Edwards NridgHt 166900.8181 0.001 154928.3244 178873.3119 True Edwards Somerst 96147.4377 0.001 86109.0265 106185.8489 True Edwards StoneBr 140876.7951 0.001 120930.3595 160823.2307 True IDOTRR NAmes 46906.2992 0.001 30938.4656 62874.1327 True IDOTRR NridgHt 193887.5709 0.001 175837.2467 211937.8951 True IDOTRR Somerst 123134.1904 0.001 106304.3569 139964.0239 True IDOTRR StoneBr 167863.5478 0.001 143773.4277 191953.6679 True NAmes NridgHt 146981.2717 0.001 135420.8475 158541.6959 True NAmes Somerst 76227.8912 0.001 66684.7 85771.0825 True NAmes StoneBr 120957.2486 0.001 101255.3936 140659.1037 True NridgHt Somerst -70753.3805 0.001 -83477.9627 -58028.7982 True NridgHt StoneBr -26024.0231 0.0064 -47448.4388 -4599.6074 True Somerst StoneBr 44729.3574 0.001 24322.6262 65136.0886 True ------------------------------------------------------------------
sns.countplot(data.Neighborhood)
plt.title('Count plot of Neighborhood')
Text(0.5, 1.0, 'Count plot of Neighborhood')
sns.catplot(x = 'Neighborhood', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Neighborhood vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Neighborhood vs Property_Sale_Price')
sns.boxplot(x = 'Neighborhood', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Neighborhood vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Neighborhood vs Property_Sale_Price')
Good Predictor
data.Condition1.isnull().sum()
0
data.Condition1.value_counts()
Norm 1794 Feedr 112 Artery 62 RRAn 39 PosN 24 RRAe 15 PosA 13 RRNn 12 RRNe 2 Name: Condition1, dtype: int64
data.Condition1.value_counts().sum()
2073
sns.countplot(data.Condition1)
plt.title('Count plot of Condition1')
Text(0.5, 1.0, 'Count plot of Condition1')
data.groupby('Condition1')['Property_Sale_Price'].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Condition1 | ||||||||
| Artery | 62.0 | 131616.129032 | 48191.170518 | 66500.0 | 105000.0 | 121450.0 | 142500.0 | 340000.0 |
| Feedr | 112.0 | 141863.616071 | 39016.050482 | 40000.0 | 121900.0 | 141000.0 | 163000.0 | 244600.0 |
| Norm | 1794.0 | 180415.495541 | 67958.906586 | 34900.0 | 131000.0 | 167370.0 | 219500.0 | 340000.0 |
| PosA | 13.0 | 242000.000000 | 59026.830058 | 180000.0 | 190000.0 | 242000.0 | 250000.0 | 335000.0 |
| PosN | 24.0 | 209645.833333 | 61215.803083 | 109500.0 | 164625.0 | 198750.0 | 264875.0 | 340000.0 |
| RRAe | 15.0 | 148433.333333 | 36689.696231 | 87000.0 | 132650.0 | 143900.0 | 158500.0 | 256300.0 |
| RRAn | 39.0 | 184348.051282 | 48809.506292 | 79500.0 | 159500.0 | 178900.0 | 192140.0 | 340000.0 |
| RRNe | 2.0 | 190750.000000 | 5303.300859 | 187000.0 | 188875.0 | 190750.0 | 192625.0 | 194500.0 |
| RRNn | 12.0 | 205000.000000 | 90036.356293 | 110000.0 | 110000.0 | 214000.0 | 297500.0 | 320000.0 |
mod_Condition1= ols("Property_Sale_Price~Condition1",data =data).fit()
aov_table_Condition1 = sm.stats.anova_lm(mod_Condition1,type = 2)
print(aov_table_Condition1)
df sum_sq mean_sq F PR(>F) Condition1 8.0 3.908084e+11 4.885105e+10 11.306153 8.731315e-16 Residual 2064.0 8.918026e+12 4.320749e+09 NaN NaN
tukey_Condition1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition1, alpha = 0.05)
print(tukey_Condition1)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================ group1 group2 meandiff p-adj lower upper reject ---------------------------------------------------------------- Artery Feedr 10247.487 0.9 -22061.0574 42556.0314 False Artery Norm 48799.3665 0.001 22434.259 75164.474 True Artery PosA 110383.871 0.001 48123.6389 172644.103 True Artery PosN 78029.7043 0.001 28962.0401 127097.3685 True Artery RRAe 16817.2043 0.9 -41911.6478 75546.0564 False Artery RRAn 52731.9222 0.0029 11018.083 94445.7615 True Artery RRNe 59133.871 0.9 -87497.4573 205765.1992 False Artery RRNn 73383.871 0.0122 9014.8333 137752.9086 True Feedr Norm 38551.8795 0.001 18673.14 58430.6189 True Feedr PosA 100136.3839 0.001 40333.5254 159939.2424 True Feedr PosN 67782.2173 0.001 21872.7296 113691.7049 True Feedr RRAe 6569.7173 0.9 -49547.3342 62686.7687 False Feedr RRAn 42484.4352 0.0152 4535.9205 80432.95 True Feedr RRNe 48886.3839 0.9 -96718.5329 194491.3008 False Feedr RRNn 63136.3839 0.0422 1141.0744 125131.6934 True Norm PosA 61584.5045 0.0221 4772.017 118396.992 True Norm PosN 29230.3378 0.4329 -12709.5943 71170.2699 False Norm RRAe -31982.1622 0.612 -84900.9709 20936.6465 False Norm RRAn 3932.5557 0.9 -29103.2827 36968.3942 False Norm RRNe 10334.5045 0.9 -134067.9493 154736.9583 False Norm RRNn 24584.5045 0.9 -34531.443 83700.452 False PosA PosN -32354.1667 0.8831 -102640.5488 37932.2154 False PosA RRAe -93566.6667 0.0055 -170907.5867 -16225.7466 True PosA RRAn -57651.9487 0.1346 -123016.9564 7713.0589 False PosA RRNe -51250.0 0.9 -206276.7276 103776.7276 False PosA RRNn -37000.0 0.8971 -118706.2596 44706.2596 False PosN RRAe -61212.5 0.1076 -128390.7486 5965.7486 False PosN RRAn -25297.7821 0.8503 -78249.4902 27653.9261 False PosN RRNe -18895.8333 0.9 -169110.9639 131319.2972 False PosN RRNn -4645.8333 0.9 -76806.8479 67515.1812 False RRAe RRAn 35914.7179 0.6594 -26095.9731 97925.409 False RRAe RRNe 42316.6667 0.9 -111325.866 195959.1993 False RRAe RRNn 56566.6667 0.3939 -22481.7642 135615.0976 False RRAn RRNe 6401.9487 0.9 -141574.381 154378.2784 False RRAn RRNn 20651.9487 0.9 -46724.759 88028.6564 False RRNe RRNn 14250.0 0.9 -141635.608 170135.608 False ----------------------------------------------------------------
data['Condition1'].replace('Artery', 'Feedr', inplace = True)
data['Condition1'].replace('RRAe', 'Feedr', inplace = True)
data['Condition1'].replace('RRNe', 'Feedr', inplace = True)
data['Condition1'].replace('RRAn', 'Norm', inplace = True)
data['Condition1'].replace('RRNn', 'PosN', inplace = True)
data['Condition1'].replace('PosA', 'PosN', inplace = True)
data.Condition1.value_counts()
Norm 1833 Feedr 191 PosN 49 Name: Condition1, dtype: int64
mod_Condition1= ols("Property_Sale_Price~Condition1",data =data).fit()
aov_table_Condition1 = sm.stats.anova_lm(mod_Condition1,type = 2)
print(aov_table_Condition1)
df sum_sq mean_sq F PR(>F) Condition1 2.0 3.681387e+11 1.840694e+11 42.616775 7.288981e-19 Residual 2070.0 8.940695e+12 4.319177e+09 NaN NaN
tukey_Condition1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition1, alpha = 0.05)
print(tukey_Condition1)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =========================================================== group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------- Feedr Norm 40934.1146 0.001 29214.1142 52654.115 True Feedr PosN 77526.7844 0.001 52843.0317 102210.537 True Norm PosN 36592.6698 0.001 14280.0366 58905.303 True -----------------------------------------------------------
sns.countplot(data.Condition1)
plt.title('Count plot of Condition1')
Text(0.5, 1.0, 'Count plot of Condition1')
sns.catplot(x = 'Condition1', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Condition1 vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Condition1 vs Property_Sale_Price')
sns.boxplot(x = 'Condition1', y = 'Property_Sale_Price', data = data)
plt.title('Box plot of Condition1 vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of Condition1 vs Property_Sale_Price')
Good Predictor
data.Condition2.isnull().sum()
0
data.Condition2.value_counts()
Norm 2054 Feedr 9 RRNn 2 PosN 2 RRAn 2 Artery 2 RRAe 1 PosA 1 Name: Condition2, dtype: int64
data.Condition2.value_counts().sum()
2073
sns.countplot(data.Condition2)
plt.title('Count plot of Condition2')
Text(0.5, 1.0, 'Count plot of Condition2')
data.groupby('Condition2')['Property_Sale_Price'].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Condition2 | ||||||||
| Artery | 2.0 | 106500.000000 | 16263.455967 | 95000.0 | 100750.0 | 106500.0 | 112250.0 | 118000.0 |
| Feedr | 9.0 | 127444.444444 | 36866.689271 | 79500.0 | 85000.0 | 128000.0 | 167500.0 | 167500.0 |
| Norm | 2054.0 | 177839.502434 | 66936.808031 | 34900.0 | 130000.0 | 164000.0 | 214000.0 | 340000.0 |
| PosA | 1.0 | 325000.000000 | NaN | 325000.0 | 325000.0 | 325000.0 | 325000.0 | 325000.0 |
| PosN | 2.0 | 262375.000000 | 109778.327779 | 184750.0 | 223562.5 | 262375.0 | 301187.5 | 340000.0 |
| RRAe | 1.0 | 190000.000000 | NaN | 190000.0 | 190000.0 | 190000.0 | 190000.0 | 190000.0 |
| RRAn | 2.0 | 136905.000000 | 0.000000 | 136905.0 | 136905.0 | 136905.0 | 136905.0 | 136905.0 |
| RRNn | 2.0 | 96750.000000 | 39951.533137 | 68500.0 | 82625.0 | 96750.0 | 110875.0 | 125000.0 |
mod = ols('Property_Sale_Price~Condition2', data = data).fit()
anova_Cond2 = sm.stats.anova_lm(mod)
print(anova_Cond2)
df sum_sq mean_sq F PR(>F) Condition2 7.0 8.550808e+10 1.221544e+10 2.734901 0.007943 Residual 2065.0 9.223326e+12 4.466502e+09 NaN NaN
tukey_Cond2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition2)
print(tukey_Cond2)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================ group1 group2 meandiff p-adj lower upper reject ---------------------------------------------------------------- Artery Feedr 20944.4444 0.9 -137565.6618 179454.5507 False Artery Norm 71339.5024 0.7772 -72108.0705 214787.0754 False Artery PosA 218500.0 0.1321 -29837.6097 466837.6097 False Artery PosN 155875.0 0.2765 -46891.8092 358641.8092 False Artery RRAe 83500.0 0.9 -164837.6097 331837.6097 False Artery RRAn 30405.0 0.9 -172361.8092 233171.8092 False Artery RRNn -9750.0 0.9 -212516.8092 193016.8092 False Feedr Norm 50395.058 0.318 -17341.7936 118131.9096 False Feedr PosA 197555.5556 0.0942 -16179.4281 411290.5392 False Feedr PosN 134930.5556 0.1629 -23579.5507 293440.6618 False Feedr RRAe 62555.5556 0.9 -151179.4281 276290.5392 False Feedr RRAn 9460.5556 0.9 -149049.5507 167970.6618 False Feedr RRNn -30694.4444 0.9 -189204.5507 127815.6618 False Norm PosA 147160.4976 0.3518 -55655.6646 349976.6598 False Norm PosN 84535.4976 0.6106 -58912.0754 227983.0705 False Norm RRAe 12160.4976 0.9 -190655.6646 214976.6598 False Norm RRAn -40934.5024 0.9 -184382.0754 102513.0705 False Norm RRNn -81089.5024 0.6541 -224537.0754 62358.0705 False PosA PosN -62625.0 0.9 -310962.6097 185712.6097 False PosA RRAe -135000.0 0.8253 -421755.5716 151755.5716 False PosA RRAn -188095.0 0.2952 -436432.6097 60242.6097 False PosA RRNn -228250.0 0.0982 -476587.6097 20087.6097 False PosN RRAe -72375.0 0.9 -320712.6097 175962.6097 False PosN RRAn -125470.0 0.5572 -328236.8092 77296.8092 False PosN RRNn -165625.0 0.2052 -368391.8092 37141.8092 False RRAe RRAn -53095.0 0.9 -301432.6097 195242.6097 False RRAe RRNn -93250.0 0.9 -341587.6097 155087.6097 False RRAn RRNn -40155.0 0.9 -242921.8092 162611.8092 False ----------------------------------------------------------------
data['Condition2'].replace('Artery', 'Feedr', inplace = True)
data['Condition2'].replace('RRAe', 'Feedr', inplace = True)
data['Condition2'].replace('RRAn', 'Feedr', inplace = True)
data['Condition2'].replace('RRNn', 'Feedr', inplace = True)
data['Condition2'].replace('PosA', 'PosN', inplace = True)
data.Condition2.value_counts()
Norm 2054 Feedr 16 PosN 3 Name: Condition2, dtype: int64
mod = ols('Property_Sale_Price~Condition2', data = data).fit()
anova_Cond2 = sm.stats.anova_lm(mod)
print(anova_Cond2)
df sum_sq mean_sq F PR(>F) Condition2 2.0 7.606935e+10 3.803467e+10 8.527432 0.000205 Residual 2070.0 9.232765e+12 4.460273e+09 NaN NaN
tukey_Cond2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition2)
print(tukey_Cond2)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ============================================================== group1 group2 meandiff p-adj lower upper reject -------------------------------------------------------------- Feedr Norm 51757.6274 0.0058 12445.5959 91069.6589 True Feedr PosN 157168.125 0.001 58618.1072 255718.1428 True Norm PosN 105410.4976 0.0175 14908.7794 195912.2157 True --------------------------------------------------------------
sns.countplot(data.Condition2)
plt.title('Count plot of Condition2')
Text(0.5, 1.0, 'Count plot of Condition2')
sns.catplot(x = 'Condition2', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Condition2 vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Condition2 vs Property_Sale_Price')
sns.boxplot(x = 'Condition2', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Condition2 vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Condition2 vs Property_Sale_Price')
Good Predictor
data.Dwelling_Type.isnull().sum()
0
data.Dwelling_Type.value_counts()
1Fam 1719 TwnhsE 168 Duplex 87 Twnhs 55 2fmCon 44 Name: Dwelling_Type, dtype: int64
data.Dwelling_Type.value_counts().sum()
2073
sns.countplot(data.Dwelling_Type)
plt.title('Count plot of Dwelling_Type')
Text(0.5, 1.0, 'Count plot of Dwelling_Type')
data.groupby('Dwelling_Type')['Property_Sale_Price'].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Dwelling_Type | ||||||||
| 1Fam | 1719.0 | 181941.322862 | 68783.487306 | 34900.0 | 132000.0 | 169900.0 | 222500.0 | 340000.0 |
| 2fmCon | 44.0 | 128177.272727 | 31443.645519 | 55000.0 | 113000.0 | 125000.0 | 140000.0 | 228950.0 |
| Duplex | 87.0 | 132676.172414 | 25563.912932 | 82000.0 | 118250.0 | 136500.0 | 145000.0 | 206300.0 |
| Twnhs | 55.0 | 136501.818182 | 41303.212551 | 75000.0 | 95750.0 | 146000.0 | 172750.0 | 230000.0 |
| TwnhsE | 168.0 | 182750.220238 | 58782.206749 | 75500.0 | 143750.0 | 173733.0 | 208900.0 | 340000.0 |
mod_Dwelling_Type = ols('Property_Sale_Price~Dwelling_Type', data = data).fit()
anova_Dwelling_Type = sm.stats.anova_lm(mod)
print(anova_Dwelling_Type)
df sum_sq mean_sq F PR(>F) Condition2 2.0 7.606935e+10 3.803467e+10 8.527432 0.000205 Residual 2070.0 9.232765e+12 4.460273e+09 NaN NaN
tukey_Dwelling_Type = pairwise_tukeyhsd(data.Property_Sale_Price, data.Dwelling_Type)
print(tukey_Dwelling_Type)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ============================================================== group1 group2 meandiff p-adj lower upper reject -------------------------------------------------------------- 1Fam 2fmCon -53764.0501 0.001 -81102.5467 -26425.5536 True 1Fam Duplex -49265.1504 0.001 -68942.8378 -29587.4631 True 1Fam Twnhs -45439.5047 0.001 -69967.9641 -20911.0453 True 1Fam TwnhsE 808.8974 0.9 -13665.6875 15283.4823 False 2fmCon Duplex 4498.8997 0.9 -28626.5994 37624.3988 False 2fmCon Twnhs 8324.5455 0.9 -27893.3042 44542.3951 False 2fmCon TwnhsE 54572.9475 0.001 24248.0251 84897.8699 True Duplex Twnhs 3825.6458 0.9 -27021.5684 34672.8599 False Duplex TwnhsE 50074.0478 0.001 26422.0097 73726.0859 True Twnhs TwnhsE 46248.4021 0.001 18430.1913 74066.6128 True --------------------------------------------------------------
data['Dwelling_Type'].replace('1Fam', 'TwnhsE', inplace = True)
data['Dwelling_Type'].replace('2fmCon', 'Duplex', inplace = True)
data['Dwelling_Type'].replace('Twnhs', 'Duplex', inplace = True)
data.Dwelling_Type.value_counts()
TwnhsE 1887 Duplex 186 Name: Dwelling_Type, dtype: int64
data.Dwelling_Type.value_counts().sum()
2073
df1=data[data.Dwelling_Type=='TwnhsE']
df2=data[data.Dwelling_Type=='Duplex']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=9.780816776783727, pvalue=4.0657590717491367e-22)
sns.countplot(data.Dwelling_Type)
plt.title('Count plot of Dwelling_Type')
Text(0.5, 1.0, 'Count plot of Dwelling_Type')
sns.catplot(x = 'Dwelling_Type', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Dwelling_Type vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Dwelling_Type vs Property_Sale_Price')
sns.boxplot(x = 'Dwelling_Type', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Dwelling_Type vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Dwelling_Type vs Property_Sale_Price')
Good Predictor
data.HouseStyle.isnull().sum()
0
data.HouseStyle.value_counts()
1Story 1036 2Story 638 1.5Fin 214 SLvl 89 SFoyer 54 2.5Unf 16 1.5Unf 16 2.5Fin 10 Name: HouseStyle, dtype: int64
data.HouseStyle.value_counts().sum()
2073
sns.countplot(data.HouseStyle)
plt.title('Count plot of HouseStyle')
Text(0.5, 1.0, 'Count plot of HouseStyle')
data.groupby("HouseStyle")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| HouseStyle | ||||||||
| 1.5Fin | 214.0 | 143526.939252 | 50472.360301 | 37900.0 | 115000.00 | 135000.0 | 160000.0 | 340000.0 |
| 1.5Unf | 16.0 | 126343.750000 | 60112.017309 | 76000.0 | 101525.00 | 115500.0 | 132750.0 | 340000.0 |
| 1Story | 1036.0 | 172477.168919 | 67414.299476 | 34900.0 | 126131.25 | 154750.0 | 208900.0 | 340000.0 |
| 2.5Fin | 10.0 | 194050.000000 | 75491.519023 | 104000.0 | 133750.00 | 194000.0 | 199250.0 | 340000.0 |
| 2.5Unf | 16.0 | 155609.375000 | 54983.379970 | 101000.0 | 128000.00 | 133900.0 | 163250.0 | 325000.0 |
| 2Story | 638.0 | 204188.871473 | 66991.000845 | 35311.0 | 160000.00 | 190000.0 | 244400.0 | 340000.0 |
| SFoyer | 54.0 | 136689.925926 | 29355.435667 | 75500.0 | 129000.00 | 135480.0 | 148000.0 | 206300.0 |
| SLvl | 89.0 | 164525.617978 | 37307.939380 | 91000.0 | 142500.00 | 160000.0 | 178000.0 | 340000.0 |
mod_HouseStyle= ols("Property_Sale_Price~HouseStyle",data =data).fit()
aov_table_HouseStyle = sm.stats.anova_lm(mod_HouseStyle,type = 2)
print(aov_table_HouseStyle)
df sum_sq mean_sq F PR(>F) HouseStyle 7.0 8.847505e+11 1.263929e+11 30.982766 4.870702e-41 Residual 2065.0 8.424084e+12 4.079459e+09 NaN NaN
tukey_HouseStyle = pairwise_tukeyhsd(data.Property_Sale_Price, data.HouseStyle, alpha = 0.05)
print(tukey_HouseStyle)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================ group1 group2 meandiff p-adj lower upper reject ---------------------------------------------------------------- 1.5Fin 1.5Unf -17183.1893 0.9 -67407.2079 33040.8294 False 1.5Fin 1Story 28950.2297 0.001 14399.5707 43500.8886 True 1.5Fin 2.5Fin 50523.0607 0.2203 -12171.7423 113217.8638 False 1.5Fin 2.5Unf 12082.4357 0.9 -38141.5829 62306.4544 False 1.5Fin 2Story 60661.9322 0.001 45353.9838 75969.8807 True 1.5Fin SFoyer -6837.0133 0.9 -36347.6141 22673.5874 False 1.5Fin SLvl 20998.6787 0.154 -3443.1583 45440.5158 False 1.5Unf 1Story 46133.4189 0.0802 -2684.8551 94951.693 False 1.5Unf 2.5Fin 67706.25 0.1457 -10409.9489 145822.4489 False 1.5Unf 2.5Unf 29265.625 0.9 -39246.8137 97778.0637 False 1.5Unf 2Story 77845.1215 0.001 28795.8046 126894.4384 True 1.5Unf SFoyer 10346.1759 0.9 -44811.5775 65503.9293 False 1.5Unf SLvl 38181.868 0.3518 -14438.5235 90802.2595 False 1Story 2.5Fin 21572.8311 0.9 -40001.5967 83147.2589 False 1Story 2.5Unf -16867.7939 0.9 -65686.068 31950.4801 False 1Story 2Story 31711.7026 0.001 21959.5176 41463.8875 True 1Story SFoyer -35787.243 0.0016 -62836.2242 -8738.2618 True 1Story SLvl -7951.5509 0.9 -29356.5789 13453.4771 False 2.5Fin 2.5Unf -38440.625 0.7867 -116556.8239 39675.5739 False 2.5Fin 2Story 10138.8715 0.9 -51618.8954 71896.6383 False 2.5Fin SFoyer -57360.0741 0.1532 -124072.6219 9352.4737 False 2.5Fin SLvl -29524.382 0.8506 -94154.8074 35106.0433 False 2.5Unf 2Story 48579.4965 0.0545 -469.8204 97628.8134 False 2.5Unf SFoyer -18919.4491 0.9 -74077.2025 36238.3043 False 2.5Unf SLvl 8916.243 0.9 -43704.1485 61536.6345 False 2Story SFoyer -67498.9455 0.001 -94962.7215 -40035.1696 True 2Story SLvl -39663.2535 0.001 -61590.1054 -17736.4016 True SFoyer SLvl 27835.6921 0.1845 -5590.781 61262.1651 False ----------------------------------------------------------------
data["HouseStyle"].replace("1.5Unf","1.5Fin",inplace = True)
data["HouseStyle"].replace("2.5Unf","1.5Fin",inplace = True)
data["HouseStyle"].replace("SFoyer","1.5Fin",inplace = True)
data["HouseStyle"].replace("2.5Fin","1.2Story",inplace = True)
data["HouseStyle"].replace('1.2Story','2Story',inplace = True)
data["HouseStyle"].replace('SLvl','1Story',inplace = True)
data.HouseStyle.value_counts()
1Story 1125 2Story 648 1.5Fin 300 Name: HouseStyle, dtype: int64
data.HouseStyle.value_counts().sum()
2073
mod_HouseStyle= ols("Property_Sale_Price~HouseStyle",data =data).fit()
aov_table_HouseStyle = sm.stats.anova_lm(mod_HouseStyle,type = 2)
print(aov_table_HouseStyle)
df sum_sq mean_sq F PR(>F) HouseStyle 2.0 8.696497e+11 4.348248e+11 106.655731 8.210510e-45 Residual 2070.0 8.439185e+12 4.076901e+09 NaN NaN
tukey_HouseStyle = pairwise_tukeyhsd(data.Property_Sale_Price, data.HouseStyle, alpha = 0.05)
print(tukey_HouseStyle)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =========================================================== group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------- 1.5Fin 1Story 29823.8762 0.001 20092.9221 39554.8303 True 1.5Fin 2Story 62008.1707 0.001 51550.3566 72465.9849 True 1Story 2Story 32184.2945 0.001 24798.8697 39569.7194 True -----------------------------------------------------------
sns.countplot(data.HouseStyle)
plt.title('Count plot of HouseStyle')
Text(0.5, 1.0, 'Count plot of HouseStyle')
sns.catplot(x = 'HouseStyle', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs HouseStyle')
Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs HouseStyle')
sns.boxplot(x = 'HouseStyle', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Sale_Price vs HouseStyle')
Text(0.5, 1.0, 'Box Plot of Property_Sale_Price vs HouseStyle')
Good Predictor
data.OverallQual.isnull().sum()
0
data.OverallQual.describe()
count 2073.000000 mean 6.233478 std 1.517115 min 1.000000 25% 5.000000 50% 6.000000 75% 7.000000 max 10.000000 Name: OverallQual, dtype: float64
data.OverallQual.value_counts()
5 508 6 463 7 416 8 265 4 214 9 164 3 20 10 17 2 4 1 2 Name: OverallQual, dtype: int64
data.OverallQual.value_counts().sum()
2073
sns.countplot(data.OverallQual)
plt.title('Count plot of OverallQual')
Text(0.5, 1.0, 'Count plot of OverallQual')
data.groupby("OverallQual")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| OverallQual | ||||||||
| 1 | 2.0 | 50150.000000 | 15344.217152 | 39300.0 | 44725.0 | 50150.0 | 55575.00 | 61000.0 |
| 2 | 4.0 | 47655.500000 | 14254.200796 | 35311.0 | 35311.0 | 47655.5 | 60000.00 | 60000.0 |
| 3 | 20.0 | 87473.750000 | 24688.567399 | 37900.0 | 75500.0 | 86250.0 | 97500.00 | 139600.0 |
| 4 | 214.0 | 136667.672897 | 58078.734600 | 34900.0 | 101850.0 | 124250.0 | 153750.00 | 340000.0 |
| 5 | 508.0 | 142626.137795 | 43095.950014 | 55993.0 | 119500.0 | 135250.0 | 154925.00 | 340000.0 |
| 6 | 463.0 | 166339.904968 | 43392.607283 | 75000.0 | 139750.0 | 163000.0 | 185000.00 | 340000.0 |
| 7 | 416.0 | 202321.163462 | 51486.310520 | 64500.0 | 172350.0 | 197000.0 | 230000.00 | 340000.0 |
| 8 | 265.0 | 237304.981132 | 69148.120764 | 64500.0 | 184100.0 | 245000.0 | 287000.00 | 340000.0 |
| 9 | 164.0 | 212890.189024 | 89622.432861 | 37900.0 | 137875.0 | 187500.0 | 316140.25 | 340000.0 |
| 10 | 17.0 | 319250.000000 | 55573.290572 | 160000.0 | 340000.0 | 340000.0 | 340000.00 | 340000.0 |
mod_OverallQual= ols("Property_Sale_Price~OverallQual",data =data).fit()
aov_table_OverallQual = sm.stats.anova_lm(mod_OverallQual,type = 2)
print(aov_table_OverallQual)
df sum_sq mean_sq F PR(>F) OverallQual 1.0 2.676231e+12 2.676231e+12 835.640689 1.193209e-154 Residual 2071.0 6.632604e+12 3.202609e+09 NaN NaN
tukey_OverallQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallQual, alpha = 0.05)
print(tukey_OverallQual)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------
1 2 -2494.5 0.9 -153629.5891 148640.5891 False
1 3 37323.75 0.9 -92100.608 166748.108 False
1 4 86517.6729 0.453 -37458.9111 210494.2569 False
1 5 92476.1378 0.3459 -31167.823 216120.0986 False
1 6 116189.905 0.087 -7477.6168 239857.4267 False
1 7 152171.1635 0.004 28473.5979 275868.729 True
1 8 187154.9811 0.001 63288.9077 311021.0546 True
1 9 162740.189 0.0014 38588.7388 286891.6392 True
1 10 269100.0 0.001 138641.6094 399558.3906 True
2 3 39818.25 0.9 -55767.9732 135404.4732 False
2 4 89012.1729 0.0452 942.5698 177081.776 True
2 5 94970.6378 0.0214 7369.892 182571.3836 True
2 6 118684.405 0.001 31050.4073 206318.4027 True
2 7 154665.6635 0.001 66989.2736 242342.0533 True
2 8 189649.4811 0.001 101735.5132 277563.4491 True
2 9 165234.689 0.001 76919.0947 253550.2834 True
2 10 271594.5 0.001 174612.7837 368576.2163 True
3 4 49193.9229 0.0054 8388.2336 89999.6122 True
3 5 55152.3878 0.001 15368.7235 94936.0521 True
3 6 78866.155 0.001 39009.3257 118722.9843 True
3 7 114847.4135 0.001 74897.462 154797.3649 True
3 8 149831.2311 0.001 109362.5389 190299.9233 True
3 9 125416.439 0.001 84082.5124 166750.3657 True
3 10 231776.25 0.001 174206.27 289346.23 True
4 5 5958.4649 0.9 -8263.6614 20180.5912 False
4 6 29672.2321 0.001 15246.7071 44097.757 True
4 7 65653.4906 0.001 50972.6339 80334.3472 True
4 8 100637.3082 0.001 84598.4811 116676.1354 True
4 9 76222.5161 0.001 58111.1269 94333.9054 True
4 10 182582.3271 0.001 138606.9758 226557.6784 True
5 6 23713.7672 0.001 12500.7642 34926.7702 True
5 7 59695.0257 0.001 48155.3889 71234.6624 True
5 8 94678.8433 0.001 81454.6366 107903.05 True
5 9 70264.0512 0.001 54590.5677 85937.5347 True
5 10 176623.8622 0.001 133595.1836 219652.5408 True
6 7 35981.2585 0.001 24191.8518 47770.6652 True
6 8 70965.0762 0.001 57522.363 84407.7893 True
6 9 46550.2841 0.001 30692.0061 62408.562 True
6 10 152910.095 0.001 109813.7602 196006.4299 True
7 8 34983.8177 0.001 21267.4655 48700.1699 True
7 9 10569.0256 0.5357 -5521.865 26659.9162 False
7 10 116928.8365 0.001 73746.3649 160111.3082 True
8 9 -24414.7921 0.001 -41753.5691 -7076.0152 True
8 10 81945.0189 0.001 38282.1939 125607.8439 True
9 10 106359.811 0.001 61893.8608 150825.7612 True
----------------------------------------------------------------
data["OverallQual"].replace(1, 3,inplace = True)
data["OverallQual"].replace(2, 3 ,inplace = True)
data["OverallQual"].replace(4, 5,inplace = True)
data["OverallQual"].replace(9, 7,inplace = True)
data.OverallQual.value_counts()
5 722 7 580 6 463 8 265 3 26 10 17 Name: OverallQual, dtype: int64
data.OverallQual.value_counts().sum()
2073
mod_OverallQual= ols("Property_Sale_Price~OverallQual",data =data).fit()
aov_table_OverallQual = sm.stats.anova_lm(mod_OverallQual,type = 2)
print(aov_table_OverallQual)
df sum_sq mean_sq F PR(>F) OverallQual 1.0 2.998150e+12 2.998150e+12 983.913616 4.758552e-177 Residual 2071.0 6.310684e+12 3.047168e+09 NaN NaN
tukey_OverallQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallQual, alpha = 0.05)
print(tukey_OverallQual)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==============================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------
3 5 62383.2477 0.001 30976.7078 93789.7876 True
3 6 87863.0973 0.001 56152.6895 119573.505 True
3 7 126832.8389 0.001 95292.9464 158372.7313 True
3 8 158828.1734 0.001 126494.0234 191162.3235 True
3 10 240773.1923 0.001 191699.6216 289846.763 True
5 6 25479.8496 0.001 16112.3292 34847.37 True
5 7 64449.5912 0.001 55676.6046 73222.5777 True
5 8 96444.9257 0.001 85144.5832 107745.2682 True
5 10 178389.9446 0.001 139784.0406 216995.8486 True
6 7 38969.7416 0.001 29164.4113 48775.0719 True
6 8 70965.0762 0.001 58845.7961 83084.3563 True
6 10 152910.095 0.001 114056.5877 191763.6023 True
7 8 31995.3346 0.001 20329.4954 43661.1738 True
7 10 113940.3534 0.001 75225.8874 152654.8195 True
8 10 81945.0189 0.001 42580.7923 121309.2454 True
--------------------------------------------------------------
sns.countplot(data.OverallQual)
plt.title('Count plot of OverallQual')
Text(0.5, 1.0, 'Count plot of OverallQual')
sns.catplot(x = 'OverallQual', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs OverallQual')
Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs OverallQual')
sns.boxplot(x = 'OverallQual', y = 'Property_Sale_Price', data = data)
plt.title('Box plot of Property_Sale_Price vs OverallQual')
Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs OverallQual')
Good Predictor
data.OverallCond.isnull().sum()
0
data.OverallCond.describe()
count 2073.000000 mean 5.835022 std 1.360214 min 1.000000 25% 5.000000 50% 5.000000 75% 7.000000 max 9.000000 Name: OverallCond, dtype: float64
data.OverallCond.value_counts()
5 943 6 351 7 315 8 172 4 147 9 113 3 26 2 5 1 1 Name: OverallCond, dtype: int64
data.OverallCond.value_counts().sum()
2073
sns.countplot(data.OverallCond)
plt.title('Count plot of OverallCond')
Text(0.5, 1.0, 'Count plot of OverallCond')
data.groupby("OverallCond")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| OverallCond | ||||||||
| 1 | 1.0 | 61000.000000 | NaN | 61000.0 | 61000.0 | 61000.0 | 61000.0 | 61000.0 |
| 2 | 5.0 | 131100.000000 | 117046.144746 | 67000.0 | 76500.0 | 85000.0 | 87000.0 | 340000.0 |
| 3 | 26.0 | 99367.153846 | 45847.233272 | 35311.0 | 69250.0 | 87250.0 | 129250.0 | 200624.0 |
| 4 | 147.0 | 163496.204082 | 68911.443393 | 37900.0 | 117500.0 | 150000.0 | 192140.0 | 340000.0 |
| 5 | 943.0 | 194661.337222 | 69026.850613 | 34900.0 | 142976.5 | 182900.0 | 236750.0 | 340000.0 |
| 6 | 351.0 | 158068.615385 | 51991.534275 | 37900.0 | 127000.0 | 144000.0 | 176000.0 | 340000.0 |
| 7 | 315.0 | 168894.031746 | 61511.613169 | 79900.0 | 125250.0 | 149700.0 | 199950.0 | 340000.0 |
| 8 | 172.0 | 164482.674419 | 59277.603926 | 35311.0 | 125375.0 | 152700.0 | 189237.5 | 340000.0 |
| 9 | 113.0 | 179425.176991 | 73785.301835 | 75000.0 | 125500.0 | 160000.0 | 215000.0 | 340000.0 |
mod_OverallCond= ols("Property_Sale_Price~OverallCond",data =data).fit()
aov_table_OverallCond = sm.stats.anova_lm(mod_OverallCond,type = 2)
print(aov_table_OverallCond)
df sum_sq mean_sq F PR(>F) OverallCond 1.0 3.136673e+10 3.136673e+10 7.001964 0.008204 Residual 2071.0 9.277467e+12 4.479704e+09 NaN NaN
tukey_OverallCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallCond, alpha = 0.05)
print(tukey_OverallCond)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------
1 2 70100.0 0.9 -149891.1778 290091.1778 False
1 3 38367.1538 0.9 -166281.9513 243016.259 False
1 4 102496.2041 0.7921 -99009.2624 304001.6705 False
1 5 133661.3372 0.4974 -67268.6667 334591.3411 False
1 6 97068.6154 0.841 -104040.8055 298178.0363 False
1 7 107894.0317 0.7399 -93248.0341 309036.0976 False
1 8 103482.6744 0.7824 -97923.8198 304889.1686 False
1 9 118425.177 0.6445 -83285.0164 320135.3704 False
2 3 -31732.8462 0.9 -129800.0545 66334.3622 False
2 4 32396.2041 0.9 -58929.4447 123721.8529 False
2 5 63561.3372 0.4143 -26487.4695 153610.1439 False
2 6 26968.6154 0.9 -63479.8245 117417.0553 False
2 7 37794.0317 0.9 -52726.9701 128315.0336 False
2 8 33382.6744 0.9 -57724.3892 124489.738 False
2 9 48325.177 0.7584 -43451.3083 140101.6623 False
3 4 64129.0502 0.001 21403.0388 106855.0616 True
3 5 95294.1834 0.001 55370.1868 135218.18 True
3 6 58701.4615 0.001 17884.0853 99518.8378 True
3 7 69526.8779 0.001 28548.9607 110504.7951 True
3 8 65115.5206 0.001 22858.7465 107372.2947 True
3 9 80058.0231 0.001 36376.6637 123739.3826 True
4 5 31165.1331 0.001 13357.2057 48973.0606 True
4 6 -5427.5887 0.9 -25157.1345 14301.9571 False
4 7 5397.8277 0.9 -14661.7454 25457.4007 False
4 8 986.4703 0.9 -21570.8285 23543.7691 False
4 9 15928.9729 0.5563 -9195.8741 41053.82 False
5 6 -36592.7218 0.001 -49149.331 -24036.1127 True
5 7 -25767.3055 0.001 -38836.35 -12698.2609 True
5 8 -30178.6628 0.001 -46829.344 -13527.9816 True
5 9 -15236.1602 0.3033 -35227.9434 4755.6229 False
6 7 10825.4164 0.4379 -4760.8797 26411.7124 False
6 8 6414.059 0.9 -12277.5937 25105.7118 False
6 9 21356.5616 0.0584 -364.4902 43077.6135 False
7 8 -4411.3573 0.9 -23451.0363 14628.3217 False
7 9 10531.1452 0.8494 -11490.1078 32552.3983 False
8 9 14942.5026 0.593 -9375.8207 39260.8258 False
----------------------------------------------------------------
data["OverallCond"].replace(1, 3,inplace = True)
data["OverallCond"].replace(2, 3,inplace = True)
data["OverallCond"].replace(4, 6,inplace = True)
data["OverallCond"].replace(7, 6,inplace = True)
data["OverallCond"].replace(8, 6,inplace = True)
data["OverallCond"].replace(9, 5,inplace = True)
data.OverallCond.value_counts()
5 1056 6 985 3 32 Name: OverallCond, dtype: int64
mod_OverallCond= ols("Property_Sale_Price~OverallCond",data =data).fit()
aov_table_OverallCond = sm.stats.anova_lm(mod_OverallCond,type = 2)
print(aov_table_OverallCond)
df sum_sq mean_sq F PR(>F) OverallCond 1.0 1.190641e+11 1.190641e+11 26.8322 2.434174e-07 Residual 2071.0 9.189770e+12 4.437359e+09 NaN NaN
tukey_OverallCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallCond, alpha = 0.05)
print(tukey_OverallCond)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=============================================================
group1 group2 meandiff p-adj lower upper reject
-------------------------------------------------------------
3 5 89904.5152 0.001 62647.6117 117161.4186 True
3 6 60334.1371 0.001 33048.3561 87619.9181 True
5 6 -29570.378 0.001 -36299.2218 -22841.5343 True
-------------------------------------------------------------
sns.countplot(data.OverallCond)
plt.title('Count plot of OverallCond')
Text(0.5, 1.0, 'Count plot of OverallCond')
sns.catplot(x = 'OverallCond', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs OverallCond')
Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs OverallCond')
sns.boxplot(x = 'OverallCond', y = 'Property_Sale_Price', data = data)
plt.title('Box plot of Property_Sale_Price vs OverallCond')
Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs OverallCond')
Good Predictor
data.YearBuilt.isnull().sum()
0
data.YearBuilt.value_counts()
2005 100
2006 86
2004 63
2007 58
1977 56
...
1898 1
1903 1
1913 1
1911 1
1872 1
Name: YearBuilt, Length: 117, dtype: int64
data.YearBuilt.value_counts().sum()
2073
plt.hist(data.YearBuilt)
plt.title('Hist of YearBuilt')
Text(0.5, 1.0, 'Hist of YearBuilt')
sns.distplot(data.YearBuilt)
plt.title('Mormality of YearBuilt')
Text(0.5, 1.0, 'Mormality of YearBuilt')
sns.countplot(data.YearBuilt)
plt.title('Count plot of YearBuilt')
Text(0.5, 1.0, 'Count plot of YearBuilt')
sns.boxplot(x = data.YearBuilt, data =data, color = 'r')
plt.title('Box plot of YearBuilt')
Text(0.5, 1.0, 'Box plot of YearBuilt')
IQR = data.YearBuilt.quantile(.75) - data.YearBuilt.quantile(.25)
IQR
49.0
upper_limit = data.YearBuilt.quantile(.75) + (1.5*IQR)
upper_limit
2072.5
lower_limit = data.YearBuilt.quantile(.25) - (1.5*IQR)
lower_limit
1876.5
len(data.YearBuilt[data.YearBuilt > upper_limit])
0
len(data.YearBuilt[data.YearBuilt < lower_limit])
2
data['YearBuilt'] = np.where(data['YearBuilt'] < lower_limit, lower_limit, data['YearBuilt'])
sns.boxplot(x = data.YearBuilt, data =data, color = 'g')
plt.title('Box plot of YearBuilt')
Text(0.5, 1.0, 'Box plot of YearBuilt')
sns.distplot(data.YearBuilt)
plt.title('Normality of YearBuilt')
Text(0.5, 1.0, 'Normality of YearBuilt')
np.corrcoef(data.YearBuilt, data.Property_Sale_Price)
array([[1. , 0.47415594],
[0.47415594, 1. ]])
sns.scatterplot(x = data['YearBuilt'], y = data.Property_Sale_Price, data = data )
plt.xlabel('YearBuilt')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of YearBuilt vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter plot of YearBuilt vs Property_Sale_Price')
Not Good Predictor
data.YearRemodAdd.isnull().sum()
0
data.YearRemodAdd.value_counts()
1950 223
2006 138
2007 121
2005 107
2000 79
...
2014 3
2019 2
2022 2
2012 2
2023 2
Name: YearRemodAdd, Length: 72, dtype: int64
data.YearRemodAdd.value_counts().sum()
2073
plt.hist(data.YearRemodAdd)
plt.title('Hist of YearRemodAdd')
Text(0.5, 1.0, 'Hist of YearRemodAdd')
sns.distplot(data.YearRemodAdd)
plt.title('Normality of YearRemodAdd')
Text(0.5, 1.0, 'Normality of YearRemodAdd')
sns.countplot(data.YearRemodAdd)
plt.title('Dist plot of YearRemodAdd')
Text(0.5, 1.0, 'Dist plot of YearRemodAdd')
sns.boxplot(x = data.YearRemodAdd, data =data, color = 'g')
plt.title('Box plot of YearRemodAdd')
Text(0.5, 1.0, 'Box plot of YearRemodAdd')
np.corrcoef(data.YearRemodAdd, data.Property_Sale_Price)
array([[1. , 0.51542146],
[0.51542146, 1. ]])
sns.scatterplot(x = data['YearRemodAdd'], y = data.Property_Sale_Price, data = data )
plt.xlabel('YearRemodAdd')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of YearRemodAdd vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter plot of YearRemodAdd vs Property_Sale_Price')
Not Good Predictor
data.RoofStyle.isnull().sum()
0
data.RoofStyle.value_counts()
Gable 1606 Hip 420 Flat 19 Gambrel 15 Mansard 11 Shed 2 Name: RoofStyle, dtype: int64
data.RoofStyle.value_counts().sum()
2073
sns.countplot(data.RoofStyle)
plt.title('Count plot of RoofStyle')
Text(0.5, 1.0, 'Count plot of RoofStyle')
data.groupby("RoofStyle")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| RoofStyle | ||||||||
| Flat | 19.0 | 198286.315789 | 66738.692027 | 82000.0 | 136000.0 | 235000.0 | 249000.0 | 274970.0 |
| Gable | 1606.0 | 170659.143836 | 60971.965331 | 34900.0 | 128000.0 | 160000.0 | 202500.0 | 340000.0 |
| Gambrel | 15.0 | 167900.000000 | 66468.209372 | 40000.0 | 116200.0 | 180500.0 | 207000.0 | 259500.0 |
| Hip | 420.0 | 203536.702381 | 81441.325754 | 55000.0 | 139825.0 | 175700.0 | 275000.0 | 340000.0 |
| Mansard | 11.0 | 168132.545455 | 66041.521508 | 84500.0 | 117500.0 | 152000.0 | 216000.0 | 265979.0 |
| Shed | 2.0 | 225000.000000 | 49497.474683 | 190000.0 | 207500.0 | 225000.0 | 242500.0 | 260000.0 |
mod = ols('Property_Sale_Price ~ RoofStyle', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) RoofStyle 5.0 3.749304e+11 7.498607e+10 17.349214 7.395804e-17 Residual 2067.0 8.933904e+12 4.322160e+09 NaN NaN
t_Roof_Style = pairwise_tukeyhsd(data.Property_Sale_Price, data.RoofStyle)
print(t_Roof_Style)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==================================================================
group1 group2 meandiff p-adj lower upper reject
------------------------------------------------------------------
Flat Gable -27627.172 0.4541 -70901.5578 15647.2139 False
Flat Gambrel -30386.3158 0.7361 -95155.8583 34383.2267 False
Flat Hip 5250.3866 0.9 -38732.5898 49233.363 False
Flat Mansard -30153.7703 0.8097 -101200.0409 40892.5002 False
Flat Shed 26713.6842 0.9 -112689.165 166116.5334 False
Gable Gambrel -2759.1438 0.9 -51402.8782 45884.5906 False
Gable Hip 32877.5585 0.001 22600.3278 43154.7893 True
Gable Mansard -2526.5984 0.9 -59260.1142 54206.9174 False
Gable Shed 54340.8562 0.8342 -78340.2413 187021.9536 False
Gambrel Hip 35636.7024 0.3074 -13638.4703 84911.8751 False
Gambrel Mansard 232.5455 0.9 -74206.1071 74671.198 False
Gambrel Shed 57100.0 0.8425 -84061.9453 198261.9453 False
Hip Mansard -35404.1569 0.4902 -92679.9944 21871.6806 False
Hip Shed 21463.2976 0.9 -111450.5969 154377.1922 False
Mansard Shed 56867.4545 0.8589 -87282.3762 201017.2853 False
------------------------------------------------------------------
data["RoofStyle"].replace('Flat', 'Hip',inplace = True)
data["RoofStyle"].replace('Shed', 'Hip',inplace = True)
data["RoofStyle"].replace('Gambrel', 'Gable',inplace = True)
data["RoofStyle"].replace('Mansard', 'Gable',inplace = True)
data.RoofStyle.value_counts()
Gable 1632 Hip 441 Name: RoofStyle, dtype: int64
df1=data[data.RoofStyle=='Gable']
df2=data[data.RoofStyle=='Hip']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=-9.301767992439983, pvalue=3.396220835555546e-20)
sns.countplot(data.RoofStyle)
plt.title('Count plot of RoofStyle')
Text(0.5, 1.0, 'Count plot of RoofStyle')
sns.catplot(x = 'RoofStyle', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs RoofStyle')
Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs RoofStyle')
sns.boxplot(x = 'RoofStyle', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Sale_Price vs RoofStyle')
Text(0.5, 1.0, 'Box Plot of Property_Sale_Price vs RoofStyle')
Good Predictor
data.RoofMatl.isnull().sum()
0
data.RoofMatl.value_counts()
CompShg 2036 Tar&Grv 17 WdShngl 10 WdShake 6 Metal 1 Roll 1 ClyTile 1 Membran 1 Name: RoofMatl, dtype: int64
data.RoofMatl.value_counts().sum()
2073
sns.countplot(data.RoofMatl)
plt.title('Count Plot of RoofMatl')
Text(0.5, 1.0, 'Count Plot of RoofMatl')
data.groupby("RoofMatl")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| RoofMatl | ||||||||
| ClyTile | 1.0 | 160000.000000 | NaN | 160000.0 | 160000.0 | 160000.0 | 160000.0 | 160000.0 |
| CompShg | 2036.0 | 176628.908644 | 66477.719608 | 34900.0 | 130000.0 | 163000.0 | 212925.0 | 340000.0 |
| Membran | 1.0 | 241500.000000 | NaN | 241500.0 | 241500.0 | 241500.0 | 241500.0 | 241500.0 |
| Metal | 1.0 | 180000.000000 | NaN | 180000.0 | 180000.0 | 180000.0 | 180000.0 | 180000.0 |
| Roll | 1.0 | 137000.000000 | NaN | 137000.0 | 137000.0 | 137000.0 | 137000.0 | 137000.0 |
| Tar&Grv | 17.0 | 192702.352941 | 69462.739880 | 82000.0 | 129000.0 | 185000.0 | 256000.0 | 274970.0 |
| WdShake | 6.0 | 249000.000000 | 37363.083385 | 190000.0 | 231500.0 | 251000.0 | 280250.0 | 287000.0 |
| WdShngl | 10.0 | 304450.000000 | 55839.278290 | 168500.0 | 278500.0 | 340000.0 | 340000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ RoofMatl', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) RoofMatl 7.0 2.033413e+11 2.904876e+10 6.587858 1.016746e-07 Residual 2065.0 9.105493e+12 4.409440e+09 NaN NaN
t_RoofMatl = pairwise_tukeyhsd(data.Property_Sale_Price, data.RoofMatl)
print(t_RoofMatl)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================== group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------------ ClyTile CompShg 16628.9086 0.9 -184887.9765 218145.7938 False ClyTile Membran 81500.0 0.9 -203417.9506 366417.9506 False ClyTile Metal 20000.0 0.9 -264917.9506 304917.9506 False ClyTile Roll -23000.0 0.9 -307917.9506 261917.9506 False ClyTile Tar&Grv 32702.3529 0.9 -174605.9068 240010.6126 False ClyTile WdShake 89000.0 0.9 -128609.6792 306609.6792 False ClyTile WdShngl 144450.0 0.4349 -66850.8074 355750.8074 False CompShg Membran 64871.0914 0.9 -136645.7938 266387.9765 False CompShg Metal 3371.0914 0.9 -198145.7938 204887.9765 False CompShg Roll -39628.9086 0.9 -241145.7938 161887.9765 False CompShg Tar&Grv 16073.4443 0.9 -32993.153 65140.0416 False CompShg WdShake 72371.0914 0.1334 -9998.7389 154740.9216 False CompShg WdShngl 127821.0914 0.001 63955.2347 191686.948 True Membran Metal -61500.0 0.9 -346417.9506 223417.9506 False Membran Roll -104500.0 0.9 -389417.9506 180417.9506 False Membran Tar&Grv -48797.6471 0.9 -256105.9068 158510.6126 False Membran WdShake 7500.0 0.9 -210109.6792 225109.6792 False Membran WdShngl 62950.0 0.9 -148350.8074 274250.8074 False Metal Roll -43000.0 0.9 -327917.9506 241917.9506 False Metal Tar&Grv 12702.3529 0.9 -194605.9068 220010.6126 False Metal WdShake 69000.0 0.9 -148609.6792 286609.6792 False Metal WdShngl 124450.0 0.6112 -86850.8074 335750.8074 False Roll Tar&Grv 55702.3529 0.9 -151605.9068 263010.6126 False Roll WdShake 112000.0 0.7458 -105609.6792 329609.6792 False Roll WdShngl 167450.0 0.2394 -43850.8074 378750.8074 False Tar&Grv WdShake 56297.6471 0.6121 -39370.7828 151966.0769 False Tar&Grv WdShngl 111747.6471 0.001 31457.5033 192037.7908 True WdShake WdShngl 55450.0 0.7126 -48587.3257 159487.3257 False ------------------------------------------------------------------
data["RoofMatl"].replace('ClyTile', 'CompShg',inplace = True)
data["RoofMatl"].replace('Membran', 'CompShg',inplace = True)
data["RoofMatl"].replace('Metal', 'CompShg',inplace = True)
data["RoofMatl"].replace('Roll', 'CompShg',inplace = True)
data["RoofMatl"].replace('Tar&Grv', 'CompShg',inplace = True)
data["RoofMatl"].replace('WdShake', 'WdShngl',inplace = True)
data.RoofMatl.value_counts()
CompShg 2057 WdShngl 16 Name: RoofMatl, dtype: int64
data.RoofMatl.value_counts().sum()
2073
df1=data[data.RoofMatl =='CompShg']
df2=data[data.RoofMatl =='WdShngl']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=-6.415414097229135, pvalue=1.7352766367863425e-10)
sns.countplot(data.RoofMatl)
plt.title('Count Plot of RoofMatl')
Text(0.5, 1.0, 'Count Plot of RoofMatl')
sns.catplot(x = 'RoofMatl', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of RoofMatl vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of RoofMatl vs Property_Sale_Price')
sns.boxplot(x = 'RoofMatl', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of RoofMatl vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of RoofMatl vs Property_Sale_Price')
Good Predictor
data.Exterior1st.isnull().sum()
0
data.Exterior1st.value_counts()
VinylSd 750 HdBoard 316 MetalSd 308 Wd Sdng 285 Plywood 156 CemntBd 87 BrkFace 72 WdShing 37 Stucco 28 AsbShng 24 Stone 3 BrkComm 3 AsphShn 2 CBlock 1 ImStucc 1 Name: Exterior1st, dtype: int64
data.Exterior1st.value_counts().sum()
2073
sns.countplot(data.Exterior1st)
plt.title('Count plot of Exterior1st')
Text(0.5, 1.0, 'Count plot of Exterior1st')
data.groupby("Exterior1st")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Exterior1st | ||||||||
| AsbShng | 24.0 | 103680.541667 | 38013.525554 | 35311.0 | 81625.0 | 108000.0 | 135000.0 | 165500.0 |
| AsphShn | 2.0 | 100000.000000 | 0.000000 | 100000.0 | 100000.0 | 100000.0 | 100000.0 | 100000.0 |
| BrkComm | 3.0 | 74666.666667 | 12701.705922 | 60000.0 | 71000.0 | 82000.0 | 82000.0 | 82000.0 |
| BrkFace | 72.0 | 187479.861111 | 75316.777333 | 40000.0 | 138500.0 | 160000.0 | 246625.0 | 340000.0 |
| CBlock | 1.0 | 105000.000000 | NaN | 105000.0 | 105000.0 | 105000.0 | 105000.0 | 105000.0 |
| CemntBd | 87.0 | 214235.344828 | 93453.373330 | 75000.0 | 117250.0 | 229456.0 | 291045.0 | 340000.0 |
| HdBoard | 316.0 | 158908.348101 | 44061.480945 | 83000.0 | 130000.0 | 147750.0 | 179200.0 | 340000.0 |
| ImStucc | 1.0 | 262000.000000 | NaN | 262000.0 | 262000.0 | 262000.0 | 262000.0 | 262000.0 |
| MetalSd | 308.0 | 150353.814935 | 53640.877044 | 62383.0 | 119000.0 | 140000.0 | 167125.0 | 340000.0 |
| Plywood | 156.0 | 172811.660256 | 48213.268088 | 82500.0 | 143187.5 | 164250.0 | 197500.0 | 340000.0 |
| Stone | 3.0 | 268000.000000 | 32908.965344 | 230000.0 | 258500.0 | 287000.0 | 287000.0 | 287000.0 |
| Stucco | 28.0 | 161579.607143 | 74703.555441 | 58500.0 | 111500.0 | 146500.0 | 191550.0 | 340000.0 |
| VinylSd | 750.0 | 208089.890667 | 66237.103227 | 37900.0 | 162000.0 | 199900.0 | 248757.0 | 340000.0 |
| Wd Sdng | 285.0 | 147160.978947 | 57668.328575 | 34900.0 | 110000.0 | 135000.0 | 169000.0 | 340000.0 |
| WdShing | 37.0 | 159834.648649 | 78941.789121 | 87000.0 | 115000.0 | 129900.0 | 172500.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ Exterior1st', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Exterior1st 14.0 1.658449e+12 1.184607e+11 31.866638 1.146734e-77 Residual 2058.0 7.650385e+12 3.717388e+09 NaN NaN
t_Exterior1st = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior1st)
print(t_Exterior1st)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =================================================================== group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------------- AsbShng AsphShn -3680.5417 0.9 -156049.3745 148688.2912 False AsbShng BrkComm -29013.875 0.9 -155792.4072 97764.6572 False AsbShng BrkFace 83799.3194 0.001 35002.2396 132596.3992 True AsbShng CBlock 1319.4583 0.9 -209978.0954 212617.012 False AsbShng CemntBd 110554.8032 0.001 62820.9681 158288.6382 True AsbShng HdBoard 55227.8064 0.0018 11392.8699 99062.7429 True AsbShng ImStucc 158319.4583 0.4134 -52978.0954 369617.012 False AsbShng MetalSd 46673.2733 0.0245 2798.1703 90548.3762 True AsbShng Plywood 69131.1186 0.001 23737.1223 114525.1149 True AsbShng Stone 164319.4583 0.0011 37540.9261 291097.9905 True AsbShng Stucco 57899.0655 0.0473 309.0599 115489.0711 True AsbShng VinylSd 104409.349 0.001 61479.0105 147339.6875 True AsbShng Wd Sdng 43480.4373 0.0568 -522.4602 87483.3347 False AsbShng WdShing 56154.107 0.0342 1893.0162 110415.1978 True AsphShn BrkComm -25333.3333 0.9 -214323.6107 163656.9441 False AsphShn BrkFace 87479.8611 0.769 -60930.6632 235890.3854 False AsphShn CBlock 5000.0 0.9 -248557.0644 258557.0644 False AsphShn CemntBd 114235.3448 0.3588 -33828.9916 262299.6813 False AsphShn HdBoard 58908.3481 0.9 -87945.424 205762.1202 False AsphShn ImStucc 162000.0 0.6611 -91557.0644 415557.0644 False AsphShn MetalSd 50353.8149 0.9 -96511.9516 197219.5815 False AsphShn Plywood 72811.6603 0.9 -74514.9959 220138.3165 False AsphShn Stone 168000.0 0.147 -20990.2774 356990.2774 False AsphShn Stucco 61579.6071 0.9 -89949.7216 213108.9359 False AsphShn VinylSd 108089.8907 0.4427 -38496.4071 254676.1884 False AsphShn Wd Sdng 47160.9789 0.9 -99743.0159 194064.9738 False AsphShn WdShing 59834.6486 0.9 -90461.0422 210130.3395 False BrkComm BrkFace 112813.1944 0.1066 -9179.5051 234805.8939 False BrkComm CBlock 30333.3333 0.9 -208722.5596 269389.2262 False BrkComm CemntBd 139568.6782 0.0086 17997.3709 261139.9854 True BrkComm HdBoard 84241.6814 0.5247 -35852.3042 204335.6671 False BrkComm ImStucc 187333.3333 0.3304 -51722.5596 426389.2262 False BrkComm MetalSd 75687.1483 0.6802 -44421.5042 195795.8007 False BrkComm Plywood 98144.9936 0.2691 -22526.787 218816.7742 False BrkComm Stone 193333.3333 0.0091 24295.2904 362371.3763 True BrkComm Stucco 86912.9405 0.5474 -38855.3881 212681.2691 False BrkComm VinylSd 133423.224 0.0133 13656.4602 253189.9878 True BrkComm Wd Sdng 72494.3123 0.7387 -47661.0818 192649.7063 False BrkComm WdShing 85167.982 0.56 -39111.2589 209447.2229 False BrkFace CBlock -82479.8611 0.9 -290941.0774 125981.3552 False BrkFace CemntBd 26755.4837 0.2734 -6228.4746 59739.442 False BrkFace HdBoard -28571.513 0.0265 -55607.1221 -1535.9039 True BrkFace ImStucc 74520.1389 0.9 -133941.0774 282981.3552 False BrkFace MetalSd -37126.0462 0.001 -64226.7317 -10025.3606 True BrkFace Plywood -14668.2009 0.9 -44164.5973 14828.1956 False BrkFace Stone 80520.1389 0.615 -41472.5606 202512.8384 False BrkFace Stucco -25900.254 0.8295 -72009.1603 20208.6524 False BrkFace VinylSd 20610.0296 0.282 -4932.8064 46152.8655 False BrkFace Wd Sdng -40318.8822 0.001 -67625.9781 -13011.7863 True BrkFace WdShing -27645.2125 0.6148 -69522.2763 14231.8514 False CBlock CemntBd 109235.3448 0.9 -98979.5507 317450.2403 False CBlock HdBoard 53908.3481 0.9 -153447.446 261264.1422 False CBlock ImStucc 157000.0 0.885 -135782.4788 449782.4788 False CBlock MetalSd 45353.8149 0.9 -162010.4741 252718.1039 False CBlock Plywood 67811.6603 0.9 -139879.3087 275502.6293 False CBlock Stone 163000.0 0.5675 -76055.8929 402055.8929 False CBlock Stucco 56579.6071 0.9 -154113.3743 267272.5886 False CBlock VinylSd 103089.8907 0.9 -104076.5585 310256.3399 False CBlock Wd Sdng 42160.9789 0.9 -165230.387 249552.3449 False CBlock WdShing 54834.6486 0.9 -154972.8576 264642.1549 False CemntBd HdBoard -55326.9967 0.001 -80392.6738 -30261.3196 True CemntBd ImStucc 47764.6552 0.9 -160450.2403 255979.5507 False CemntBd MetalSd -63881.5299 0.001 -89017.3841 -38745.6757 True CemntBd Plywood -41423.6846 0.001 -69125.6955 -13721.6736 True CemntBd Stone 53764.6552 0.9 -67806.6521 175335.9624 False CemntBd Stucco -52655.7377 0.0064 -97637.9037 -7673.5717 True CemntBd VinylSd -6145.4542 0.9 -29593.2788 17302.3704 False CemntBd Wd Sdng -67074.3659 0.001 -92432.6287 -41716.103 True CemntBd WdShing -54400.6962 0.001 -95033.8413 -13767.551 True HdBoard ImStucc 103091.6519 0.9 -104264.1422 310447.446 False HdBoard MetalSd -8554.5332 0.9 -25131.4394 8022.3731 False HdBoard Plywood 13903.3122 0.5577 -6354.6262 34161.2505 False HdBoard Stone 109091.6519 0.1245 -11002.3338 229185.6376 False HdBoard Stucco 2671.259 0.9 -38150.0333 43492.5514 False HdBoard VinylSd 49181.5426 0.001 35296.9061 63066.1791 True HdBoard Wd Sdng -11747.3692 0.5397 -28659.6176 5164.8793 False HdBoard WdShing 926.3005 0.9 -35046.3994 36899.0005 False ImStucc MetalSd -111646.1851 0.8803 -319010.4741 95718.1039 False ImStucc Plywood -89188.3397 0.9 -296879.3087 118502.6293 False ImStucc Stone 6000.0 0.9 -233055.8929 245055.8929 False ImStucc Stucco -100420.3929 0.9 -311113.3743 110272.5886 False ImStucc VinylSd -53910.1093 0.9 -261076.5585 153256.3399 False ImStucc Wd Sdng -114839.0211 0.8469 -322230.387 92552.3449 False ImStucc WdShing -102165.3514 0.9 -311972.8576 107642.1549 False MetalSd Plywood 22457.8453 0.0151 2113.1393 42802.5513 True MetalSd Stone 117646.1851 0.0623 -2462.4674 237754.8375 False MetalSd Stucco 11225.7922 0.9 -29638.6288 52090.2132 False MetalSd VinylSd 57736.0757 0.001 43725.1468 71747.0046 True MetalSd Wd Sdng -3192.836 0.9 -20208.9209 13823.2489 False MetalSd WdShing 9480.8337 0.9 -26540.8006 45502.468 False Plywood Stone 95188.3397 0.3191 -25483.4409 215860.1204 False Plywood Stucco -11232.0531 0.9 -53723.1245 31259.0183 False Plywood VinylSd 35278.2304 0.001 17060.2071 53496.2537 True Plywood Wd Sdng -25650.6813 0.0023 -46269.5414 -5031.8212 True Plywood WdShing -12977.0116 0.9 -50833.9579 24879.9347 False Stone Stucco -106420.3929 0.2099 -232188.7215 19347.9358 False Stone VinylSd -59910.1093 0.9 -179676.8731 59856.6544 False Stone Wd Sdng -120839.0211 0.0471 -240994.4151 -683.627 True Stone WdShing -108165.3514 0.1728 -232444.5922 16113.8895 False Stucco VinylSd 46510.2835 0.0067 6661.9433 86358.6237 True Stucco Wd Sdng -14418.6282 0.9 -55420.2286 26582.9722 False Stucco WdShing -1744.9585 0.9 -53601.8985 50111.9815 False VinylSd Wd Sdng -60928.9117 0.001 -75335.0374 -46522.7861 True VinylSd WdShing -48255.242 0.001 -83119.9441 -13390.54 True Wd Sdng WdShing 12673.6697 0.9 -23503.5119 48850.8513 False -------------------------------------------------------------------
data["Exterior1st"].replace('AsphShn', 'AsbShng',inplace = True)
data["Exterior1st"].replace('BrkComm', 'AsbShng',inplace = True)
data["Exterior1st"].replace('CBlock', 'AsbShng',inplace = True)
data["Exterior1st"].replace('ImStucc', 'BrkFace',inplace = True)
data["Exterior1st"].replace('CemntBd', 'VinylSd',inplace = True)
data["Exterior1st"].replace('Stone', 'VinylSd',inplace = True)
data["Exterior1st"].replace('Stucco', 'HdBoard',inplace = True)
data["Exterior1st"].replace('WdShing', 'HdBoard',inplace = True)
data["Exterior1st"].replace('Wd Sdng', 'MetalSd',inplace = True)
data["Exterior1st"].replace('BrkFace', 'Plywood',inplace = True)
data["Exterior1st"].replace('HdBoard', 'MetalSd',inplace = True)
data.Exterior1st.value_counts()
MetalSd 974 VinylSd 840 Plywood 229 AsbShng 30 Name: Exterior1st, dtype: int64
data.Exterior1st.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ Exterior1st', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Exterior1st 3.0 1.598341e+12 5.327802e+11 142.963912 3.488991e-84 Residual 2069.0 7.710493e+12 3.726676e+09 NaN NaN
t_Exterior1st = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior1st)
print(t_Exterior1st)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- AsbShng MetalSd 52300.0598 0.001 23205.5802 81394.5394 True AsbShng Plywood 77235.1984 0.001 46759.3814 107711.0153 True AsbShng VinylSd 108362.5821 0.001 79198.8434 137526.3209 True MetalSd Plywood 24935.1386 0.001 13408.0551 36462.222 True MetalSd VinylSd 56062.5223 0.001 48671.86 63453.1847 True Plywood VinylSd 31127.3837 0.001 19426.5903 42828.1771 True ---------------------------------------------------------------
sns.countplot(data.Exterior1st)
plt.title('Count plot of Exterior1st')
Text(0.5, 1.0, 'Count plot of Exterior1st')
sns.catplot('Exterior1st', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Property_Sale_Price vs Exterior1st')
Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs Exterior1st')
sns.boxplot('Exterior1st', 'Property_Sale_Price', data=data)
plt.title('Box plot of Property_Sale_Price vs Exterior1st')
Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs Exterior1st')
Good Predictor
data.Exterior2nd.isnull().sum()
0
data.Exterior2nd.value_counts()
VinylSd 729 MetalSd 299 HdBoard 292 Wd Sdng 277 Plywood 205 CmentBd 85 Wd Shng 55 BrkFace 35 Stucco 29 AsbShng 27 ImStucc 14 Brk Cmn 11 Stone 9 AsphShn 4 CBlock 1 Other 1 Name: Exterior2nd, dtype: int64
data.Exterior2nd.value_counts().sum()
2073
sns.countplot(data.Exterior2nd)
plt.title('Count plot of Exterior2nd')
Text(0.5, 1.0, 'Count plot of Exterior2nd')
data.groupby("Exterior2nd")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Exterior2nd | ||||||||
| AsbShng | 27.0 | 110716.037037 | 42612.862368 | 35311.0 | 90000.0 | 122500.0 | 135000.0 | 225000.0 |
| AsphShn | 4.0 | 128500.000000 | 36041.642582 | 100000.0 | 100000.0 | 119500.0 | 148000.0 | 175000.0 |
| Brk Cmn | 11.0 | 130363.636364 | 36381.376355 | 60000.0 | 114000.0 | 148500.0 | 155000.0 | 155000.0 |
| BrkFace | 35.0 | 193470.000000 | 76279.933565 | 40000.0 | 147500.0 | 160000.0 | 250000.0 | 340000.0 |
| CBlock | 1.0 | 105000.000000 | NaN | 105000.0 | 105000.0 | 105000.0 | 105000.0 | 105000.0 |
| CmentBd | 85.0 | 213299.705882 | 93417.583395 | 75000.0 | 115000.0 | 229456.0 | 287090.0 | 340000.0 |
| HdBoard | 292.0 | 165542.595890 | 49759.903194 | 83000.0 | 131475.0 | 157000.0 | 185000.0 | 340000.0 |
| ImStucc | 14.0 | 230692.857143 | 97939.608270 | 88000.0 | 135000.0 | 229000.0 | 320000.0 | 340000.0 |
| MetalSd | 299.0 | 150934.698997 | 53912.357252 | 62383.0 | 119000.0 | 140000.0 | 166000.0 | 340000.0 |
| Other | 1.0 | 319000.000000 | NaN | 319000.0 | 319000.0 | 319000.0 | 319000.0 | 319000.0 |
| Plywood | 205.0 | 165383.175610 | 46085.008061 | 76500.0 | 134432.0 | 154000.0 | 193000.0 | 320000.0 |
| Stone | 9.0 | 128291.555556 | 57265.806043 | 90000.0 | 90000.0 | 93500.0 | 177000.0 | 230000.0 |
| Stucco | 29.0 | 156310.793103 | 70876.371844 | 58500.0 | 90350.0 | 144000.0 | 200100.0 | 340000.0 |
| VinylSd | 729.0 | 208688.524005 | 65998.120466 | 37900.0 | 163990.0 | 200000.0 | 248900.0 | 340000.0 |
| Wd Sdng | 277.0 | 146030.523466 | 56313.471191 | 34900.0 | 110000.0 | 135000.0 | 164900.0 | 340000.0 |
| Wd Shng | 55.0 | 163871.818182 | 75437.407684 | 55000.0 | 120000.0 | 137450.0 | 173000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ Exterior2nd', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Exterior2nd 15.0 1.648526e+12 1.099017e+11 29.511593 2.410342e-76 Residual 2057.0 7.660308e+12 3.724019e+09 NaN NaN
t_Exterior2nd = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior2nd)
print(t_Exterior2nd)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =================================================================== group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------------- AsbShng AsphShn 17783.963 0.9 -94374.4658 129942.3917 False AsbShng Brk Cmn 19647.5993 0.9 -55234.243 94529.4417 False AsbShng BrkFace 82753.963 0.001 29131.9778 136375.9481 True AsbShng CBlock -5716.037 0.9 -218902.7242 207470.6502 False AsbShng CmentBd 102583.6688 0.001 56336.9832 148830.3545 True AsbShng HdBoard 54826.5589 0.001 12716.5841 96936.5336 True AsbShng ImStucc 119976.8201 0.001 51030.8359 188922.8044 True AsbShng MetalSd 40218.662 0.0799 -1849.5711 82286.895 False AsbShng Other 208283.963 0.0642 -4902.7242 421470.6502 False AsbShng Plywood 54667.1386 0.0014 11807.5372 97526.7399 True AsbShng Stone 17575.5185 0.9 -63001.4754 98152.5124 False AsbShng Stucco 45594.7561 0.2737 -10390.8108 101580.3229 False AsbShng VinylSd 97972.487 0.001 56944.6899 139000.284 True AsbShng Wd Sdng 35314.4864 0.2313 -6891.8807 77520.8536 False AsbShng Wd Shng 53155.7811 0.0197 3962.403 102349.1593 True AsphShn Brk Cmn 1863.6364 0.9 -120367.6096 124094.8823 False AsphShn BrkFace 64970.0 0.7882 -45522.1013 175462.1013 False AsphShn CBlock -23500.0 0.9 -257555.0165 210555.0165 False AsphShn CmentBd 84799.7059 0.321 -22307.4512 191906.863 False AsphShn HdBoard 37042.5959 0.9 -68344.4865 142429.6783 False AsphShn ImStucc 102192.8571 0.1899 -16494.6987 220880.413 False AsphShn MetalSd 22434.699 0.9 -82935.7115 127805.1094 False AsphShn Other 190500.0 0.2747 -43555.0165 424555.0165 False AsphShn Plywood 36883.1756 0.9 -68805.6724 142572.0236 False AsphShn Stone -208.4444 0.9 -126009.2358 125592.3469 False AsphShn Stucco 27810.7931 0.9 -83847.4841 139469.0703 False AsphShn VinylSd 80188.524 0.3879 -24770.8362 185147.8842 False AsphShn Wd Sdng 17530.5235 0.9 -87895.1119 122956.1588 False AsphShn Wd Shng 35371.8182 0.9 -73040.2458 143783.8822 False Brk Cmn BrkFace 63106.3636 0.1737 -9255.7924 135468.5197 False Brk Cmn CBlock -25363.6364 0.9 -244017.5363 193290.2635 False Brk Cmn CmentBd 82936.0695 0.0024 15856.1152 150016.0239 True Brk Cmn HdBoard 35178.9595 0.8789 -29118.8967 99476.8158 False Brk Cmn ImStucc 100329.2208 0.0048 15981.6477 184676.7939 True Brk Cmn MetalSd 20571.0626 0.9 -43699.4638 84841.5891 False Brk Cmn Other 188636.3636 0.1872 -30017.5363 407290.2635 False Brk Cmn Plywood 35019.5392 0.8935 -29771.7393 99810.8178 False Brk Cmn Stone -2072.0808 0.9 -96165.7378 92021.5762 False Brk Cmn Stucco 25947.1567 0.9 -48183.458 100077.7715 False Brk Cmn VinylSd 78324.8876 0.0026 14730.5131 141919.2622 True Brk Cmn Wd Sdng 15666.8871 0.9 -48694.1397 80027.9139 False Brk Cmn Wd Shng 33508.1818 0.9 -35636.2525 102652.6161 False BrkFace CBlock -88470.0 0.9 -300784.7543 123844.7543 False BrkFace CmentBd 19829.7059 0.9 -22214.8806 61874.2924 False BrkFace HdBoard -27927.4041 0.4337 -65373.9165 9519.1083 False BrkFace ImStucc 37222.8571 0.8453 -28977.8986 103423.6129 False BrkFace MetalSd -42535.301 0.0096 -79934.8672 -5135.7348 True BrkFace Other 125530.0 0.7811 -86784.7543 337844.7543 False BrkFace Plywood -28086.8244 0.4638 -66374.3778 10200.729 False BrkFace Stone -65178.4444 0.2375 -143419.3753 13062.4864 False BrkFace Stucco -37159.2069 0.5247 -89727.0204 15408.6066 False BrkFace VinylSd 15218.524 0.9 -21006.7634 51443.8115 False BrkFace Wd Sdng -47439.4765 0.0017 -84994.353 -9884.6001 True BrkFace Wd Shng -29598.1818 0.6422 -74863.8395 15667.4759 False CBlock CmentBd 108299.7059 0.9 -102273.3065 318872.7183 False CBlock HdBoard 60542.5959 0.9 -149160.7365 270245.9282 False CBlock ImStucc 125692.8571 0.8059 -90999.9816 342385.6959 False CBlock MetalSd 45934.699 0.9 -163760.2553 255629.6533 False CBlock Other 214000.0 0.4891 -82058.78 510058.78 False CBlock Plywood 60383.1756 0.9 -149471.9722 270238.3234 False CBlock Stone 23291.5556 0.9 -197377.6303 243960.7414 False CBlock Stucco 51310.7931 0.9 -161613.187 264234.7732 False CBlock VinylSd 103688.524 0.9 -105800.1816 313177.2296 False CBlock Wd Sdng 41030.5235 0.9 -168692.1864 250753.2334 False CBlock Wd Shng 58871.8182 0.9 -152367.9178 270111.5542 False CmentBd HdBoard -47757.11 0.001 -73557.8839 -21956.336 True CmentBd ImStucc 17393.1513 0.9 -42988.789 77775.0915 False CmentBd MetalSd -62365.0069 0.001 -88097.597 -36632.4168 True CmentBd Other 105700.2941 0.9 -104872.7183 316273.3065 False CmentBd Plywood -47916.5303 0.001 -74923.4811 -20909.5795 True CmentBd Stone -85008.1503 0.0073 -158391.2676 -11625.033 True CmentBd Stucco -56988.9128 0.0016 -102009.06 -11968.7655 True CmentBd VinylSd -4611.1819 0.9 -28605.1414 19382.7777 False CmentBd Wd Sdng -67269.1824 0.001 -93226.9826 -41311.3822 True CmentBd Wd Shng -49427.8877 0.001 -85655.2036 -13200.5718 True HdBoard ImStucc 65150.2613 0.0096 7874.8499 122425.6726 True HdBoard MetalSd -14607.8969 0.2112 -31831.7244 2615.9306 False HdBoard Other 153457.4041 0.4681 -56245.9282 363160.7365 False HdBoard Plywood -159.4203 0.9 -19234.7821 18915.9416 False HdBoard Stone -37251.0403 0.9 -108100.0061 33597.9254 False HdBoard Stucco -9231.8028 0.9 -49990.9453 31527.3397 False HdBoard VinylSd 43145.9281 0.001 28647.5097 57644.3465 True HdBoard Wd Sdng -19512.0724 0.0135 -37070.5869 -1953.5579 True HdBoard Wd Shng -1670.7777 0.9 -32442.7204 29101.1649 False ImStucc MetalSd -79758.1581 0.001 -137002.8872 -22513.4291 True ImStucc Other 88307.1429 0.9 -128385.6959 304999.9816 False ImStucc Plywood -65309.6815 0.0107 -123138.465 -7480.898 True ImStucc Stone -102401.3016 0.0087 -191843.3203 -12959.2829 True ImStucc Stucco -74382.064 0.0172 -142511.4004 -6252.7277 True ImStucc VinylSd -22004.3331 0.9 -78488.87 34480.2037 False ImStucc Wd Sdng -84662.3337 0.001 -142008.6518 -27316.0156 True ImStucc Wd Shng -66821.039 0.0234 -129488.5044 -4153.5735 True MetalSd Other 168065.301 0.3004 -41629.6533 377760.2553 False MetalSd Plywood 14448.4766 0.3951 -4534.5602 33431.5135 False MetalSd Stone -22643.1434 0.9 -93467.3074 48181.0205 False MetalSd Stucco 5376.0941 0.9 -35339.9219 46092.1101 False MetalSd VinylSd 57753.825 0.001 43377.094 72130.556 True MetalSd Wd Sdng -4904.1755 0.9 -22362.3452 12553.9942 False MetalSd Wd Shng 12937.1192 0.9 -17777.6771 43651.9155 False Other Plywood -153616.8244 0.4675 -363471.9722 56238.3234 False Other Stone -190708.4444 0.1848 -411377.6303 29960.7414 False Other Stucco -162689.2069 0.3877 -375613.187 50234.7732 False Other VinylSd -110311.476 0.9 -319800.1816 99177.2296 False Other Wd Sdng -172969.4765 0.2532 -382692.1864 36753.2334 False Other Wd Shng -155128.1818 0.4618 -366367.9178 56111.5542 False Plywood Stone -37091.6201 0.9 -108388.6845 34205.4444 False Plywood Stucco -9072.3825 0.9 -50605.5398 32460.7748 False Plywood VinylSd 43305.3484 0.001 26755.4363 59855.2605 True Plywood Wd Sdng -19352.6521 0.0482 -38639.8729 -65.4314 True Plywood Wd Shng -1511.3574 0.9 -33301.4173 30278.7025 False Stone Stucco 28019.2375 0.9 -51860.1069 107898.582 False Stone VinylSd 80396.9684 0.0087 10185.8147 150608.1222 True Stone Wd Sdng 17738.9679 0.9 -53167.3322 88645.268 False Stone Wd Shng 35580.2626 0.9 -39694.663 110855.1882 False Stucco VinylSd 52377.7309 0.001 12737.6267 92017.8351 True Stucco Wd Sdng -10280.2696 0.9 -51138.9915 30578.4522 False Stucco Wd Shng 7561.0251 0.9 -40481.1041 55603.1543 False VinylSd Wd Sdng -62658.0005 0.001 -77434.0484 -47881.9527 True VinylSd Wd Shng -44816.7058 0.001 -74090.288 -15543.1237 True Wd Sdng Wd Shng 17841.2947 0.8119 -13062.4253 48745.0147 False -------------------------------------------------------------------
data["Exterior2nd"].replace('AsphShn', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('Brk Cmn', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('CBlock', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('Stone', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('BrkFace', 'VinylSd',inplace = True)
data["Exterior2nd"].replace('CmentBd', 'VinylSd',inplace = True)
data["Exterior2nd"].replace('Plywood', 'HdBoard',inplace = True)
data["Exterior2nd"].replace('Stucco', 'HdBoard',inplace = True)
data["Exterior2nd"].replace('Wd Shng', 'HdBoard',inplace = True)
data["Exterior2nd"].replace('Wd Sdng', 'MetalSd',inplace = True)
data["Exterior2nd"].replace('ImStucc', 'VinylSd',inplace = True)
data["Exterior2nd"].replace('Other', 'VinylSd',inplace = True)
data.Exterior2nd.value_counts()
VinylSd 864 HdBoard 581 MetalSd 576 AsbShng 52 Name: Exterior2nd, dtype: int64
data.Exterior2nd.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ Exterior2nd', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Exterior2nd 3.0 1.609325e+12 5.364417e+11 144.151782 8.010779e-85 Residual 2069.0 7.699509e+12 3.721367e+09 NaN NaN
t_Exterior2nd = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior2nd)
print(t_Exterior2nd)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- AsbShng HdBoard 45695.1373 0.001 22991.9732 68398.3014 True AsbShng MetalSd 29404.0174 0.0049 6692.76 52115.2748 True AsbShng VinylSd 89837.6562 0.001 67442.0169 112233.2956 True HdBoard MetalSd -16291.1199 0.001 -25513.4708 -7068.769 True HdBoard VinylSd 44142.519 0.001 35727.3419 52557.696 True MetalSd VinylSd 60433.6389 0.001 51996.6514 68870.6264 True -----------------------------------------------------------------
sns.countplot(data.Exterior2nd)
plt.title('Count plot of Exterior2nd')
Text(0.5, 1.0, 'Count plot of Exterior2nd')
sns.catplot('Exterior2nd', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Property_Sale_Price vs Exterior2nd')
Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs Exterior2nd')
sns.boxplot('Exterior2nd', 'Property_Sale_Price', data=data)
plt.title('Box plot of Property_Sale_Price vs Exterior2nd')
Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs Exterior2nd')
Good Predictor
data.MasVnrType.isnull().sum()
14
data.MasVnrType.dtype
dtype('O')
data.MasVnrType.mode()
0 None dtype: object
data.MasVnrType.fillna(data.MasVnrType.mode()[0] , inplace = True)
data.MasVnrType.isnull().sum()
0
data.MasVnrType.value_counts()
None 1247 BrkFace 620 Stone 186 BrkCmn 20 Name: MasVnrType, dtype: int64
data.MasVnrType.value_counts().sum()
2073
sns.countplot(data.MasVnrType)
plt.title('Count plot of MasVnrType')
Text(0.5, 1.0, 'Count plot of MasVnrType')
data.groupby("MasVnrType")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| MasVnrType | ||||||||
| BrkCmn | 20.0 | 144938.550000 | 41726.240841 | 89471.0 | 116375.0 | 147000.0 | 163475.0 | 277000.0 |
| BrkFace | 620.0 | 200255.917742 | 65695.079837 | 75000.0 | 149975.0 | 183950.0 | 236000.0 | 340000.0 |
| None | 1247.0 | 156338.171612 | 55775.467311 | 34900.0 | 119250.0 | 143500.0 | 184950.0 | 340000.0 |
| Stone | 186.0 | 248055.150538 | 69805.524683 | 119000.0 | 194000.0 | 248900.0 | 315500.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ MasVnrType', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) MasVnrType 3.0 1.826592e+12 6.088639e+11 168.363874 1.177350e-97 Residual 2069.0 7.482243e+12 3.616357e+09 NaN NaN
t_MasVnrType = pairwise_tukeyhsd(data.Property_Sale_Price, data.MasVnrType)
print(t_MasVnrType)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- BrkCmn BrkFace 55317.3677 0.001 20190.6321 90444.1034 True BrkCmn None 11399.6216 0.8131 -23450.0532 46249.2965 False BrkCmn Stone 103116.6005 0.001 66731.7339 139501.4672 True BrkFace None -43917.7461 0.001 -51515.7889 -36319.7033 True BrkFace Stone 47799.2328 0.001 34872.9441 60725.5215 True None Stone 91716.9789 0.001 79563.7398 103870.2181 True -----------------------------------------------------------------
data["MasVnrType"].replace('BrkCmn', 'None',inplace = True)
data.MasVnrType.value_counts()
None 1267 BrkFace 620 Stone 186 Name: MasVnrType, dtype: int64
data.MasVnrType.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ MasVnrType', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) MasVnrType 2.0 1.824034e+12 9.120168e+11 252.227801 9.330649e-99 Residual 2070.0 7.484801e+12 3.615846e+09 NaN NaN
t_MasVnrType = pairwise_tukeyhsd(data.Property_Sale_Price, data.MasVnrType)
print(t_MasVnrType)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- BrkFace None -44097.6928 0.001 -51010.0561 -37185.3295 True BrkFace Stone 47799.2328 0.001 36008.5228 59589.9428 True None Stone 91896.9256 0.001 80822.7149 102971.1363 True ---------------------------------------------------------------
sns.countplot(data.MasVnrType)
plt.title('Count plot of MasVnrType')
Text(0.5, 1.0, 'Count plot of MasVnrType')
sns.catplot('MasVnrType', 'Property_Sale_Price' , data=data)
plt.title('Cat plot of Property_Sale_Price vs MasVnrType')
Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs MasVnrType')
sns.boxplot('MasVnrType', 'Property_Sale_Price' , data=data)
plt.title('Box plot of Property_Sale_Price vs MasVnrType')
Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs MasVnrType')
Good Predictor
data.MasVnrArea.isnull().sum()
14
data.MasVnrArea.dtype
dtype('float64')
data.MasVnrArea.describe()
count 2059.000000 mean 101.875668 std 179.169217 min 0.000000 25% 0.000000 50% 0.000000 75% 161.500000 max 1600.000000 Name: MasVnrArea, dtype: float64
data.MasVnrArea.sample(10)
875 0.0 36 0.0 628 192.0 847 0.0 115 1129.0 820 0.0 1026 324.0 1299 0.0 534 0.0 679 61.0 Name: MasVnrArea, dtype: float64
data.MasVnrArea.fillna(data.MasVnrArea.median(), inplace = True)
data.MasVnrArea.isnull().sum()
0
data.MasVnrArea.describe()
count 2073.000000 mean 101.187651 std 178.757820 min 0.000000 25% 0.000000 50% 0.000000 75% 160.000000 max 1600.000000 Name: MasVnrArea, dtype: float64
data.MasVnrArea.value_counts()
0.0 1243
108.0 12
340.0 10
210.0 9
106.0 9
...
299.0 1
97.0 1
278.0 1
292.0 1
428.0 1
Name: MasVnrArea, Length: 342, dtype: int64
data.MasVnrArea.value_counts().sum()
2073
plt.hist(data.MasVnrArea)
plt.title('Hist of MasVnrArea')
Text(0.5, 1.0, 'Hist of MasVnrArea')
sns.distplot(data.MasVnrArea)
plt.title('Normality MasVnrArea')
Text(0.5, 1.0, 'Normality MasVnrArea')
sns.countplot(data.MasVnrArea)
plt.title('count plot of MasVnrArea')
Text(0.5, 1.0, 'count plot of MasVnrArea')
sns.boxplot(x = data.MasVnrArea, data =data, color = 'r')
plt.title('Box plot of MasVnrArea')
Text(0.5, 1.0, 'Box plot of MasVnrArea')
IQR = data.MasVnrArea.quantile(0.75) - data.MasVnrArea.quantile(0.25)
IQR
160.0
upper_limit = data.MasVnrArea.quantile(0.75) + (1.5*IQR)
upper_limit
400.0
lower_limit = data.MasVnrArea.quantile(0.25) - (1.5*IQR)
lower_limit
-240.0
len(data.MasVnrArea[data.MasVnrArea > upper_limit])
143
len(data.MasVnrArea[data.MasVnrArea < lower_limit])
0
for i in np.arange(upper_limit, 800, 200):
outliers = len(data.MasVnrArea[data.MasVnrArea > i])
print('At a limit of: ', i, 'There are ', outliers)
At a limit of: 400.0 There are 143 At a limit of: 600.0 There are 54
data['MasVnrArea'] = np.where(data['MasVnrArea'] > upper_limit, upper_limit, data['MasVnrArea'])
sns.boxplot(x = data.MasVnrArea, data =data, color = 'g')
plt.title('Box plot of MasVnrArea')
Text(0.5, 1.0, 'Box plot of MasVnrArea')
sns.distplot(data.MasVnrArea)
plt.title('Normality of MasVnrArea')
Text(0.5, 1.0, 'Normality of MasVnrArea')
np.corrcoef(data.MasVnrArea, data.Property_Sale_Price)
array([[1. , 0.45503223],
[0.45503223, 1. ]])
sns.scatterplot(x = data['MasVnrArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('MasVnrArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of MasVnrArea vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter plot of MasVnrArea vs Property_Sale_Price')
Not Good Predictor
data.ExterQual.isnull().sum()
0
data.ExterQual.value_counts()
TA 1272 Gd 706 Ex 76 Fa 19 Name: ExterQual, dtype: int64
data.ExterQual.value_counts().sum()
2073
sns.countplot(data.ExterQual)
plt.title('Count plot of ExterQual')
Text(0.5, 1.0, 'Count plot of ExterQual')
data.groupby("ExterQual")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| ExterQual | ||||||||
| Ex | 76.0 | 304193.276316 | 55255.814100 | 160000.0 | 299800.0 | 335000.0 | 340000.0 | 340000.0 |
| Fa | 19.0 | 90936.473684 | 34933.709741 | 39300.0 | 71000.0 | 85000.0 | 110000.0 | 200000.0 |
| Gd | 706.0 | 227613.082153 | 56929.057167 | 52000.0 | 184900.0 | 221250.0 | 265979.0 | 340000.0 |
| TA | 1272.0 | 143559.811321 | 41343.779478 | 34900.0 | 118768.5 | 139000.0 | 165000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ ExterQual', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) ExterQual 3.0 4.600500e+12 1.533500e+12 673.871283 1.466181e-305 Residual 2069.0 4.708334e+12 2.275657e+09 NaN NaN
t_ExterQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.ExterQual)
print(t_ExterQual)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
Ex Fa -213256.8026 0.001 -244716.5052 -181797.1001 True
Ex Gd -76580.1942 0.001 -91387.3155 -61773.0728 True
Ex TA -160633.465 0.001 -175116.8808 -146150.0491 True
Fa Gd 136676.6085 0.001 108162.0758 165191.1412 True
Fa TA 52623.3376 0.001 24275.55 80971.1253 True
Gd TA -84053.2708 0.001 -89809.5738 -78296.9679 True
-----------------------------------------------------------------
sns.catplot('ExterQual', 'Property_Sale_Price', data=data)
plt.title('Cat plot of ExterQual vs Property_sale_price')
Text(0.5, 1.0, 'Cat plot of ExterQual vs Property_sale_price')
sns.boxplot('ExterQual', 'Property_Sale_Price', data=data)
plt.title('Box plot of ExterQual vs Property_sale_price')
Text(0.5, 1.0, 'Box plot of ExterQual vs Property_sale_price')
Good Predictor
data.ExterCond.isnull().sum()
0
data.ExterCond.value_counts()
TA 1815 Gd 219 Fa 35 Ex 3 Po 1 Name: ExterCond, dtype: int64
data.ExterCond.value_counts().sum()
2073
sns.countplot(data.ExterCond)
plt.title('Count plot of ExterCond')
Text(0.5, 1.0, 'Count plot of ExterCond')
data.groupby("ExterCond")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| ExterCond | ||||||||
| Ex | 3.0 | 201333.333333 | 109235.220205 | 118000.0 | 139500.0 | 161000.0 | 243000.0 | 325000.0 |
| Fa | 35.0 | 106946.514286 | 38633.905585 | 39300.0 | 80500.0 | 115000.0 | 140000.0 | 169500.0 |
| Gd | 219.0 | 170332.031963 | 58812.747429 | 68400.0 | 130750.0 | 158000.0 | 201000.0 | 340000.0 |
| Po | 1.0 | 76500.000000 | NaN | 76500.0 | 76500.0 | 76500.0 | 76500.0 | 76500.0 |
| TA | 1815.0 | 179847.413223 | 67531.729210 | 34900.0 | 130500.0 | 167000.0 | 216250.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ ExterCond', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) ExterCond 4.0 2.073636e+11 5.184089e+10 11.779082 1.838421e-09 Residual 2068.0 9.101471e+12 4.401098e+09 NaN NaN
t_ExterCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.ExterCond)
print(t_ExterCond)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
Ex Fa -94386.819 0.1251 -203346.949 14573.3109 False
Ex Gd -31001.3014 0.9 -136285.7595 74283.1568 False
Ex Po -124833.3333 0.4793 -333974.6473 84307.9806 False
Ex TA -21485.9201 0.9 -126142.9634 83171.1232 False
Fa Gd 63385.5177 0.001 30414.5842 96356.4511 True
Fa Po -30446.5143 0.9 -214137.4355 153244.4069 False
Fa TA 72900.8989 0.001 41991.9675 103809.8304 True
Gd Po -93832.032 0.6046 -275366.7717 87702.7078 False
Gd TA 9515.3813 0.2638 -3441.0551 22471.8176 False
Po TA 103347.4132 0.5216 -77824.1666 284518.993 False
-----------------------------------------------------------------
data["ExterCond"].replace('Ex', 'TA',inplace = True)
data["ExterCond"].replace('Gd', 'TA',inplace = True)
data["ExterCond"].replace('Po', 'Fa',inplace = True)
data.ExterCond.value_counts()
TA 2037 Fa 36 Name: ExterCond, dtype: int64
data.ExterCond.value_counts().sum()
2073
df1=data[data.ExterCond=='TA']
df2=data[data.ExterCond=='Fa']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=6.520283823345744, pvalue=8.790776565581211e-11)
sns.countplot(data.ExterCond)
plt.title('Count plot of ExterCond')
Text(0.5, 1.0, 'Count plot of ExterCond')
sns.catplot('ExterCond', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of ExterCond vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of ExterCond vs Property_Sale_Price')
sns.boxplot('ExterCond', 'Property_Sale_Price', data=data)
plt.title('Box Plot of ExterCond vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of ExterCond vs Property_Sale_Price')
Good Predictor
data.Foundation.isnull().sum()
0
data.Foundation.value_counts()
PConc 928 CBlock 901 BrkTil 191 Slab 40 Stone 10 Wood 3 Name: Foundation, dtype: int64
data.Foundation.value_counts().sum()
2073
sns.countplot(data.Foundation)
plt.title('Count plot of Foundation')
Text(0.5, 1.0, 'Count plot of Foundation')
data.groupby("Foundation")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Foundation | ||||||||
| BrkTil | 191.0 | 129701.293194 | 48595.423104 | 37900.0 | 100000.0 | 124900.0 | 154400.00 | 340000.0 |
| CBlock | 901.0 | 149323.705882 | 46434.127889 | 34900.0 | 123000.0 | 141000.0 | 167900.00 | 340000.0 |
| PConc | 928.0 | 217604.738147 | 65636.968941 | 78000.0 | 173733.0 | 204750.0 | 260675.00 | 340000.0 |
| Slab | 40.0 | 110771.525000 | 31139.767500 | 39300.0 | 90000.0 | 108700.0 | 124123.00 | 198500.0 |
| Stone | 10.0 | 191073.400000 | 79767.802738 | 102776.0 | 116000.0 | 201489.5 | 266369.75 | 266500.0 |
| Wood | 3.0 | 185666.666667 | 56695.090910 | 143000.0 | 153500.0 | 164000.0 | 207000.00 | 250000.0 |
mod = ols('Property_Sale_Price ~ Foundation', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Foundation 5.0 2.824406e+12 5.648812e+11 180.063606 2.183733e-159 Residual 2067.0 6.484428e+12 3.137120e+09 NaN NaN
t_Foundation = pairwise_tukeyhsd(data.Property_Sale_Price, data.Foundation)
print(t_Foundation)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- BrkTil CBlock 19622.4127 0.001 6896.1395 32348.6859 True BrkTil PConc 87903.445 0.001 75209.5946 100597.2953 True BrkTil Slab -18929.7682 0.3769 -46709.5169 8849.9805 False BrkTil Stone 61372.1068 0.0097 9545.7898 113198.4238 True BrkTil Wood 55965.3735 0.5173 -36993.8648 148924.6117 False CBlock PConc 68281.0323 0.001 60808.9874 75753.0771 True CBlock Slab -38552.1809 0.001 -64367.1389 -12737.2229 True CBlock Stone 41749.6941 0.1768 -9050.5495 92549.9378 False CBlock Wood 36342.9608 0.8608 -56048.1513 128734.0729 False PConc Slab -106833.2131 0.001 -132632.2027 -81034.2236 True PConc Stone -26531.3381 0.6485 -77323.4691 24260.7928 False PConc Wood -31938.0715 0.9 -124324.7231 60448.5802 False Slab Stone 80301.875 0.001 23818.0617 136785.6883 True Slab Wood 74895.1417 0.2227 -20738.9213 170529.2047 False Stone Wood -5406.7333 0.9 -110573.8703 99760.4037 False -----------------------------------------------------------------
data["Foundation"].replace('Wood', 'PConc',inplace = True)
data["Foundation"].replace('Stone', 'PConc',inplace = True)
data["Foundation"].replace('Slab', 'BrkTil',inplace = True)
data.Foundation.value_counts()
PConc 941 CBlock 901 BrkTil 231 Name: Foundation, dtype: int64
data.Foundation.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ Foundation', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Foundation 2.0 2.802594e+12 1.401297e+12 445.831193 9.772675e-162 Residual 2070.0 6.506240e+12 3.143111e+09 NaN NaN
t_Foundation = pairwise_tukeyhsd(data.Property_Sale_Price, data.Foundation)
print(t_Foundation)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ============================================================ group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------ BrkTil CBlock 22900.2946 0.001 13202.9125 32597.6768 True BrkTil PConc 90797.5569 0.001 81142.3254 100452.7883 True CBlock PConc 67897.2622 0.001 61768.2952 74026.2293 True ------------------------------------------------------------
sns.countplot(data.Foundation)
plt.title('Count plot of Foundation')
Text(0.5, 1.0, 'Count plot of Foundation')
sns.catplot('Foundation', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Foundation vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of Foundation vs Property_Sale_Price')
sns.boxplot('Foundation', 'Property_Sale_Price', data=data)
plt.title('Box plot of Foundation vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of Foundation vs Property_Sale_Price')
Good Predictor
data.BsmtQual.isnull().sum()
59
data.BsmtQual.dtype
dtype('O')
data['BsmtQual'] = data["BsmtQual"].fillna("No Basement")
data.BsmtQual.isnull().sum()
0
data.BsmtQual.value_counts()
Gd 904 TA 891 Ex 169 No Basement 59 Fa 50 Name: BsmtQual, dtype: int64
data.BsmtQual.value_counts().sum()
2073
sns.countplot(data.BsmtQual)
plt.title('Count plot of BsmtQual')
Text(0.5, 1.0, 'Count plot of BsmtQual')
data.groupby("BsmtQual")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BsmtQual | ||||||||
| Ex | 169.0 | 289784.644970 | 59455.881416 | 123500.0 | 240000.0 | 318000.0 | 340000.0 | 340000.0 |
| Fa | 50.0 | 119061.420000 | 36058.605421 | 61000.0 | 91875.0 | 114000.0 | 134250.0 | 206900.0 |
| Gd | 904.0 | 201837.722345 | 54558.954794 | 75500.0 | 166830.0 | 192000.0 | 235346.0 | 340000.0 |
| No Basement | 59.0 | 106993.949153 | 28115.764591 | 39300.0 | 90000.0 | 101800.0 | 118964.0 | 198500.0 |
| TA | 891.0 | 139673.151515 | 41221.918687 | 34900.0 | 116700.0 | 135000.0 | 156500.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ BsmtQual', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtQual 4.0 4.405123e+12 1.101281e+12 464.433686 7.169002e-286 Residual 2068.0 4.903711e+12 2.371234e+09 NaN NaN
t_B = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtQual, alpha = 0.05)
print(t_B)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
============================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------------------
Ex Fa -170723.225 0.001 -192126.0361 -149320.4138 True
Ex Gd -87946.9226 0.001 -99088.57 -76805.2752 True
Ex No Basement -182790.6958 0.001 -202894.3582 -162687.0334 True
Ex TA -150111.4935 0.001 -161265.9353 -138957.0516 True
Fa Gd 82776.3023 0.001 63461.8638 102090.7408 True
Fa No Basement -12067.4708 0.6737 -37622.6752 13487.7335 False
Fa TA 20611.7315 0.0298 1289.9096 39933.5534 True
Gd No Basement -94843.7732 0.001 -112707.8279 -76979.7185 True
Gd TA -62164.5708 0.001 -68440.6191 -55888.5225 True
No Basement TA 32679.2024 0.001 14807.1651 50551.2396 True
----------------------------------------------------------------------------
data["BsmtQual"].replace('Fa', 'No Basement',inplace = True)
data.BsmtQual.value_counts()
Gd 904 TA 891 Ex 169 No Basement 109 Name: BsmtQual, dtype: int64
data.BsmtQual.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ BsmtQual', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtQual 3.0 4.401182e+12 1.467061e+12 618.492965 6.085538e-287 Residual 2069.0 4.907652e+12 2.371992e+09 NaN NaN
t_B = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtQual, alpha = 0.05)
print(t_B)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
===========================================================================
group1 group2 meandiff p-adj lower upper reject
---------------------------------------------------------------------------
Ex Gd -87946.9226 0.001 -98441.1856 -77452.6597 True
Ex No Basement -177255.1587 0.001 -192638.3148 -161872.0027 True
Ex TA -150111.4935 0.001 -160617.8075 -139605.1794 True
Gd No Basement -89308.2361 0.001 -102004.8206 -76611.6516 True
Gd TA -62164.5708 0.001 -68075.9499 -56253.1918 True
No Basement TA 27143.6653 0.001 14437.1182 39850.2123 True
---------------------------------------------------------------------------
sns.countplot(data.BsmtQual)
plt.title('Count plot of BsmtQual')
Text(0.5, 1.0, 'Count plot of BsmtQual')
sns.catplot('BsmtQual', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtQual vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of BsmtQual vs Property_Sale_Price')
sns.boxplot('BsmtQual', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtQual vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of BsmtQual vs Property_Sale_Price')
Good Predictor
data.BsmtCond.isnull().sum()
59
data.BsmtCond.dtype
dtype('O')
data.BsmtCond.fillna('No basement', inplace = True)
data.BsmtCond.isnull().sum()
0
data.BsmtCond.value_counts()
TA 1851 Gd 100 Fa 61 No basement 59 Po 2 Name: BsmtCond, dtype: int64
data.BsmtCond.value_counts().sum()
2073
sns.countplot(data.BsmtCond)
plt.title('Count plot of BsmtCond')
Text(0.5, 1.0, 'Count plot of BsmtCond')
data.groupby("BsmtCond")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BsmtCond | ||||||||
| Fa | 61.0 | 125764.885246 | 43955.279142 | 55000.0 | 96500.0 | 119200.0 | 147000.0 | 265979.0 |
| Gd | 100.0 | 213821.820000 | 60761.330363 | 119000.0 | 165750.0 | 199000.0 | 266500.0 | 340000.0 |
| No basement | 59.0 | 106993.949153 | 28115.764591 | 39300.0 | 90000.0 | 101800.0 | 118964.0 | 198500.0 |
| Po | 2.0 | 64000.000000 | 4242.640687 | 61000.0 | 62500.0 | 64000.0 | 65500.0 | 67000.0 |
| TA | 1851.0 | 179716.323609 | 66406.658696 | 34900.0 | 132500.0 | 165400.0 | 215000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ BsmtCond', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtCond 4.0 6.233294e+11 1.558324e+11 37.103348 5.283618e-30 Residual 2068.0 8.685505e+12 4.199954e+09 NaN NaN
t_BsmtCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtCond)
print(t_BsmtCond)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
===========================================================================
group1 group2 meandiff p-adj lower upper reject
---------------------------------------------------------------------------
Fa Gd 88056.9348 0.001 59312.0726 116801.7969 True
Fa No basement -18770.9361 0.5053 -51079.0726 13537.2004 False
Fa Po -61764.8852 0.6527 -188910.8464 65381.076 False
Fa TA 53951.4384 0.001 30927.054 76975.8227 True
Gd No basement -106827.8708 0.001 -135873.7661 -77781.9756 True
Gd Po -149821.82 0.0108 -276178.2357 -23465.4043 True
Gd TA -34105.4964 0.001 -52270.5898 -15940.403 True
No basement Po -42993.9492 0.8816 -170208.3052 84220.4069 False
No basement TA 72722.3745 0.001 49323.2462 96121.5027 True
Po TA 115716.3236 0.0859 -9462.744 240895.3912 False
---------------------------------------------------------------------------
data.BsmtCond.replace('Po', 'No basement', inplace = True)
data.BsmtCond.replace('Fa', 'No basement', inplace = True)
data.BsmtCond.value_counts()
TA 1851 No basement 122 Gd 100 Name: BsmtCond, dtype: int64
data.BsmtCond.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ BsmtCond', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtCond 2.0 6.073324e+11 3.036662e+11 72.23914 4.713926e-31 Residual 2070.0 8.701502e+12 4.203624e+09 NaN NaN
t_BsmtCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtCond)
print(t_BsmtCond)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=========================================================================
group1 group2 meandiff p-adj lower upper reject
-------------------------------------------------------------------------
Gd No basement -98147.2216 0.001 -118660.1809 -77634.2624 True
Gd TA -34105.4964 0.001 -49717.4471 -18493.5457 True
No basement TA 64041.7252 0.001 49827.861 78255.5895 True
-------------------------------------------------------------------------
sns.countplot(data.BsmtCond)
plt.title('Count plot of BsmtCond')
Text(0.5, 1.0, 'Count plot of BsmtCond')
sns.catplot('BsmtCond', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtCond vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of BsmtCond vs Property_Sale_Price')
sns.boxplot('BsmtCond', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtCond vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of BsmtCond vs Property_Sale_Price')
Good Predictor
data.BsmtExposure.isnull().sum()
61
data.BsmtExposure.dtype
dtype('O')
data.BsmtExposure.fillna('No Basement', inplace = True)
data.BsmtExposure.isnull().sum()
0
data.BsmtExposure.value_counts()
No 1349 Av 322 Gd 181 Mn 160 No Basement 61 Name: BsmtExposure, dtype: int64
data.BsmtExposure.value_counts().sum()
2073
sns.countplot(data.BsmtExposure)
plt.title('Count plot of BsmtExposure')
Text(0.5, 1.0, 'Count plot of BsmtExposure')
data.groupby("BsmtExposure")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BsmtExposure | ||||||||
| Av | 322.0 | 199222.850932 | 70246.764118 | 35311.0 | 144125.00 | 185675.0 | 246578.0 | 340000.0 |
| Gd | 181.0 | 234769.093923 | 78900.744173 | 61000.0 | 167900.00 | 225000.0 | 315500.0 | 340000.0 |
| Mn | 160.0 | 193995.568750 | 69158.528027 | 78000.0 | 142093.75 | 186750.0 | 239799.0 | 340000.0 |
| No | 1349.0 | 165878.057821 | 57988.554630 | 34900.0 | 125500.00 | 155000.0 | 193000.0 | 340000.0 |
| No Basement | 61.0 | 109797.426230 | 31620.647693 | 39300.0 | 90000.00 | 106250.0 | 120000.0 | 198500.0 |
mod = ols('Property_Sale_Price ~ BsmtExposure', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtExposure 4.0 1.250910e+12 3.127276e+11 80.258963 2.202829e-63 Residual 2068.0 8.057924e+12 3.896482e+09 NaN NaN
t_BsmtExposure = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtExposure)
print(t_BsmtExposure)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=====================================================================
group1 group2 meandiff p-adj lower upper reject
---------------------------------------------------------------------
Av Gd 35546.243 0.001 19713.9788 51378.5072 True
Av Mn -5227.2822 0.9 -21711.2535 11256.6892 False
Av No -33344.7931 0.001 -43914.9291 -22774.6571 True
Av No Basement -89425.4247 0.001 -113223.0046 -65627.8448 True
Gd Mn -40773.5252 0.001 -59266.3889 -22280.6614 True
Gd No -68891.0361 0.001 -82381.4947 -55400.5775 True
Gd No Basement -124971.6677 0.001 -150202.3931 -99740.9423 True
Mn No -28117.5109 0.001 -42367.1844 -13867.8374 True
Mn No Basement -84198.1425 0.001 -109842.8345 -58553.4505 True
No No Basement -56080.6316 0.001 -78388.8518 -33772.4114 True
---------------------------------------------------------------------
data.BsmtExposure.replace('Mn', 'Av', inplace = True)
data.BsmtExposure.value_counts()
No 1349 Av 482 Gd 181 No Basement 61 Name: BsmtExposure, dtype: int64
data.BsmtExposure.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ BsmtExposure', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtExposure 3.0 1.247990e+12 4.159965e+11 106.77502 2.841040e-64 Residual 2069.0 8.060845e+12 3.896010e+09 NaN NaN
t_BsmtExposure = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtExposure)
print(t_BsmtExposure)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
======================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------------
Av Gd 37281.4404 0.001 23291.1417 51271.7391 True
Av No -31609.5957 0.001 -40125.8321 -23093.3593 True
Av No Basement -87690.2273 0.001 -109499.6669 -65880.7876 True
Gd No -68891.0361 0.001 -81594.8313 -56187.2409 True
Gd No Basement -124971.6677 0.001 -148731.1245 -101212.2109 True
No No Basement -56080.6316 0.001 -77088.002 -35073.2612 True
----------------------------------------------------------------------
sns.countplot(data.BsmtExposure)
plt.title('Count plot of BsmtExposure')
Text(0.5, 1.0, 'Count plot of BsmtExposure')
sns.catplot('BsmtExposure', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtExposure vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of BsmtExposure vs Property_Sale_Price')
sns.boxplot('BsmtExposure', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtExposure vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of BsmtExposure vs Property_Sale_Price')
Good Predictor
data.BsmtFinType1.isnull().sum()
59
data.BsmtFinType1.dtype
dtype('O')
data.BsmtFinType1.fillna('No basement', inplace = True)
data.BsmtFinType1.isnull().sum()
0
data.BsmtFinType1.value_counts()
Unf 604 GLQ 599 ALQ 319 BLQ 210 Rec 173 LwQ 109 No basement 59 Name: BsmtFinType1, dtype: int64
data.BsmtFinType1.value_counts().sum()
2073
sns.countplot(data.BsmtFinType1)
plt.title('Count plot of BsmtFinType1')
Text(0.5, 1.0, 'Count plot of BsmtFinType1')
data.groupby("BsmtFinType1")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BsmtFinType1 | ||||||||
| ALQ | 319.0 | 160272.648903 | 44780.348164 | 81000.0 | 129250.0 | 148500.0 | 179400.0 | 340000.0 |
| BLQ | 210.0 | 147228.252381 | 44001.875087 | 35311.0 | 122000.0 | 140000.0 | 161500.0 | 340000.0 |
| GLQ | 599.0 | 225608.691152 | 68004.823134 | 80000.0 | 173733.0 | 214000.0 | 275000.0 | 340000.0 |
| LwQ | 109.0 | 149950.000000 | 48729.133711 | 75500.0 | 117000.0 | 139000.0 | 172500.0 | 301000.0 |
| No basement | 59.0 | 106993.949153 | 28115.764591 | 39300.0 | 90000.0 | 101800.0 | 118964.0 | 198500.0 |
| Rec | 173.0 | 147012.254335 | 42258.300502 | 34900.0 | 125000.0 | 143000.0 | 161000.0 | 340000.0 |
| Unf | 604.0 | 170322.137417 | 66293.801295 | 37900.0 | 118500.0 | 165000.0 | 207000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ BsmtFinType1', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtFinType1 6.0 2.241398e+12 3.735663e+11 109.203384 8.185389e-120 Residual 2066.0 7.067436e+12 3.420831e+09 NaN NaN
t_BsmtFinType1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType1)
print(t_BsmtFinType1)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==========================================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------------------
ALQ BLQ -13044.3965 0.1562 -28383.061 2294.268 False
ALQ GLQ 65336.0422 0.001 53372.0179 77300.0666 True
ALQ LwQ -10322.6489 0.6641 -29473.0553 8827.7575 False
ALQ No basement -53278.6998 0.001 -77740.5472 -28816.8523 True
ALQ Rec -13260.3946 0.1983 -29558.1899 3037.4008 False
ALQ Unf 10049.4885 0.1662 -1897.3155 21996.2926 False
BLQ GLQ 78380.4388 0.001 64537.8951 92222.9825 True
BLQ LwQ 2721.7476 0.9 -17655.107 23098.6022 False
BLQ No basement -40234.3032 0.001 -65667.7481 -14800.8584 True
BLQ Rec -215.998 0.9 -17938.7594 17506.7633 False
BLQ Unf 23093.885 0.001 9266.222 36921.548 True
GLQ LwQ -75658.6912 0.001 -93633.1022 -57684.2801 True
GLQ No basement -118614.742 0.001 -142167.3081 -95062.1759 True
GLQ Rec -78596.4368 0.001 -93494.7428 -63698.1309 True
GLQ Unf -55286.5537 0.001 -65239.8203 -45333.2872 True
LwQ No basement -42956.0508 0.001 -70854.5016 -15057.6001 True
LwQ Rec -2937.7457 0.9 -24046.0309 18170.5396 False
LwQ Unf 20372.1374 0.0146 2409.1838 38335.091 True
No basement Rec 40018.3052 0.001 13995.1689 66041.4415 True
No basement Unf 63328.1883 0.001 39784.3649 86872.0116 True
Rec Unf 23309.8831 0.001 8425.4023 38194.3639 True
--------------------------------------------------------------------------
data.BsmtFinType1.replace('LwQ', 'BLQ', inplace = True)
data.BsmtFinType1.replace('Rec', 'BLQ', inplace = True)
data.BsmtFinType1.replace('ALQ', 'Unf',inplace = True )
data.BsmtFinType1.value_counts()
Unf 923 GLQ 599 BLQ 492 No basement 59 Name: BsmtFinType1, dtype: int64
data.BsmtFinType1.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ BsmtFinType1', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtFinType1 3.0 2.219637e+12 7.398789e+11 215.935521 7.410086e-122 Residual 2069.0 7.089197e+12 3.426388e+09 NaN NaN
t_BsmtFinType1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType1)
print(t_BsmtFinType1)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
========================================================================
group1 group2 meandiff p-adj lower upper reject
------------------------------------------------------------------------
BLQ GLQ 77853.4005 0.001 68696.3059 87010.4951 True
BLQ No basement -40761.3415 0.001 -61496.556 -20026.127 True
BLQ Unf 19093.6216 0.001 10692.5225 27494.7207 True
GLQ No basement -118614.742 0.001 -139150.693 -98078.791 True
GLQ Unf -58759.7789 0.001 -66656.2737 -50863.2841 True
No basement Unf 59854.9631 0.001 39644.7833 80065.1429 True
------------------------------------------------------------------------
sns.countplot(data.BsmtFinType1)
plt.title('Count plot of BsmtFinType1')
Text(0.5, 1.0, 'Count plot of BsmtFinType1')
sns.catplot('BsmtFinType1', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtFinType1 vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of BsmtFinType1 vs Property_Sale_Price')
sns.boxplot('BsmtFinType1', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtFinType1 vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of BsmtFinType1 vs Property_Sale_Price')
Good Predictor
data.BsmtFinSF1.isnull().sum()
0
data.BsmtFinSF1.describe()
count 2073.000000 mean 437.949349 std 448.051119 min 0.000000 25% 0.000000 50% 381.000000 75% 708.000000 max 5644.000000 Name: BsmtFinSF1, dtype: float64
data.BsmtFinSF1.value_counts()
0 663
24 18
16 15
936 12
1200 9
...
1216 1
1198 1
1196 1
1186 1
1767 1
Name: BsmtFinSF1, Length: 637, dtype: int64
data.BsmtFinSF1.value_counts().sum()
2073
plt.hist(data.BsmtFinSF1)
plt.title('Hist of BsmtFinSF1')
Text(0.5, 1.0, 'Hist of BsmtFinSF1')
sns.distplot(data.BsmtFinSF1)
plt.title('Normality of BsmtFinSF1')
Text(0.5, 1.0, 'Normality of BsmtFinSF1')
sns.countplot(data.BsmtFinSF1)
plt.title('Count plot of BsmtFinSF1')
Text(0.5, 1.0, 'Count plot of BsmtFinSF1')
sns.boxplot(data.BsmtFinSF1, color = 'red')
plt.title('BoxPlot Of BsmtFinSF1')
Text(0.5, 1.0, 'BoxPlot Of BsmtFinSF1')
IQR = data.BsmtFinSF1.quantile(0.75) - data.BsmtFinSF1.quantile(0.25)
IQR
708.0
upper_limit = data.BsmtFinSF1.quantile(0.75) + (1.5*IQR)
upper_limit
1770.0
lower_limit = data.BsmtFinSF1.quantile(0.25) - (1.5*IQR)
lower_limit
-1062.0
len(data.BsmtFinSF1[data.BsmtFinSF1 > upper_limit])
12
len(data.BsmtFinSF1[data.BsmtFinSF1 < lower_limit])
0
for i in np.arange(upper_limit,2300,1000):
outliers = len(data.BsmtFinSF1[data.BsmtFinSF1 > i])
print('At the point of: ', i, 'There are ', outliers, 'Outliers')
At the point of: 1770.0 There are 12 Outliers
data.BsmtFinSF1 = np.where(data.BsmtFinSF1 > upper_limit, upper_limit, data.BsmtFinSF1)
sns.boxplot(data.BsmtFinSF1, color = 'g')
plt.title('BoxPlot Of BsmtFinSF1')
Text(0.5, 1.0, 'BoxPlot Of BsmtFinSF1')
np.corrcoef(data.BsmtFinSF1, data.Property_Sale_Price)
array([[1. , 0.36562477],
[0.36562477, 1. ]])
Not Good Predictor
data.BsmtFinType2.isnull().sum()
60
data.BsmtFinType2.dtype
dtype('O')
data.BsmtFinType2.fillna('No Basement', inplace = True)
data.BsmtFinType2.isnull().sum()
0
data.BsmtFinType2.value_counts()
Unf 1770 Rec 79 LwQ 68 No Basement 60 BLQ 50 ALQ 24 GLQ 22 Name: BsmtFinType2, dtype: int64
data.BsmtFinType2.value_counts().sum()
2073
sns.countplot(data.BsmtFinType2)
plt.title('Count plot of BsmtFinType2')
Text(0.5, 1.0, 'Count plot of BsmtFinType2')
data.groupby("BsmtFinType2")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BsmtFinType2 | ||||||||
| ALQ | 24.0 | 199154.166667 | 73038.304299 | 123500.0 | 131000.0 | 169700.0 | 290000.0 | 340000.0 |
| BLQ | 50.0 | 146091.660000 | 36211.174172 | 62383.0 | 129000.0 | 141000.0 | 158712.5 | 271900.0 |
| GLQ | 22.0 | 181625.000000 | 63255.799188 | 75500.0 | 114250.0 | 193000.0 | 236500.0 | 270000.0 |
| LwQ | 68.0 | 167181.617647 | 43912.261491 | 88000.0 | 138500.0 | 155500.0 | 189725.0 | 287000.0 |
| No Basement | 60.0 | 109944.050000 | 36045.577789 | 39300.0 | 90000.0 | 104025.0 | 119223.0 | 284000.0 |
| Rec | 79.0 | 161127.531646 | 48851.882623 | 85000.0 | 129000.0 | 147000.0 | 178450.0 | 340000.0 |
| Unf | 1770.0 | 181567.964407 | 68304.963259 | 34900.0 | 132500.0 | 169250.0 | 221000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ BsmtFinType2', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtFinType2 6.0 3.924698e+11 6.541164e+10 15.156452 4.704314e-17 Residual 2066.0 8.916364e+12 4.315762e+09 NaN NaN
t_BsmtFinType2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType2)
print(t_BsmtFinType2)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==========================================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------------------
ALQ BLQ -53062.5067 0.0199 -101207.6947 -4917.3186 True
ALQ GLQ -17529.1667 0.9 -74754.6678 39696.3345 False
ALQ LwQ -31972.549 0.385 -78004.7231 14059.625 False
ALQ No Basement -89210.1167 0.001 -136036.0074 -42384.2259 True
ALQ Rec -38026.635 0.1659 -83215.0487 7161.7787 False
ALQ Unf -17586.2023 0.8334 -57428.7054 22256.3009 False
BLQ GLQ 35533.34 0.3448 -14068.4736 85135.1536 False
BLQ LwQ 21089.9576 0.5863 -15028.4999 57208.4152 False
BLQ No Basement -36147.61 0.0624 -73272.3453 977.1253 False
BLQ Rec 15035.8716 0.8546 -20000.8899 50072.6332 False
BLQ Unf 35476.3044 0.0032 7673.3013 63279.3076 True
GLQ LwQ -14443.3824 0.9 -61996.9504 33110.1857 False
GLQ No Basement -71680.95 0.001 -120003.2515 -23358.6485 True
GLQ Rec -20497.4684 0.8383 -67234.7502 26239.8135 False
GLQ Unf -57.0356 0.9 -41647.9702 41533.899 False
LwQ No Basement -57237.5676 0.001 -91577.7466 -22897.3887 True
LwQ Rec -6054.086 0.9 -38125.5153 26017.3433 False
LwQ Unf 14386.3468 0.5576 -9572.1361 38344.8297 False
No Basement Rec 51183.4816 0.001 17982.8857 84384.0776 True
No Basement Unf 71623.9144 0.001 46173.7298 97074.099 True
Rec Unf 20440.4328 0.0971 -1853.9743 42734.8399 False
--------------------------------------------------------------------------
data.BsmtFinType2.replace('GLQ', 'ALQ', inplace = True)
data.BsmtFinType2.replace('LwQ', 'Rec', inplace = True)
data.BsmtFinType2.replace('ALQ', 'Unf', inplace = True)
data.BsmtFinType2.replace('BLQ', 'Rec', inplace = True)
data.BsmtFinType2.value_counts()
Unf 1816 Rec 197 No Basement 60 Name: BsmtFinType2, dtype: int64
data.BsmtFinType2.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ BsmtFinType2', data = data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtFinType2 2.0 3.719368e+11 1.859684e+11 43.074752 4.695439e-19 Residual 2070.0 8.936897e+12 4.317342e+09 NaN NaN
t_BsmtFinType2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType2)
print(t_BsmtFinType2)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------
No Basement Rec 49457.0058 0.001 26732.9501 72181.0616 True
No Basement Unf 71857.0221 0.001 51635.6374 92078.4069 True
Rec Unf 22400.0163 0.001 10839.9886 33960.044 True
----------------------------------------------------------------
sns.countplot(data.BsmtFinType2)
plt.title('Count plot of BsmtFinType2')
Text(0.5, 1.0, 'Count plot of BsmtFinType2')
sns.catplot('BsmtFinType2', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtFinType2 vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of BsmtFinType2 vs Property_Sale_Price')
sns.boxplot('BsmtFinType2', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtFinType2 vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of BsmtFinType2 vs Property_Sale_Price')
Good Predictor
data.BsmtFinSF2.isnull().sum()
0
data.BsmtFinSF2.describe()
count 2073.000000 mean 49.219489 std 165.621465 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 1474.000000 Name: BsmtFinSF2, dtype: float64
data.BsmtFinSF2.value_counts()
0 1829
93 7
290 6
180 5
712 5
...
600 1
580 1
546 1
544 1
1127 1
Name: BsmtFinSF2, Length: 144, dtype: int64
data.BsmtFinSF2.value_counts().sum()
2073
plt.hist(data.BsmtFinSF2)
plt.title('Hist of BsmtFinSF2')
Text(0.5, 1.0, 'Hist of BsmtFinSF2')
sns.distplot(data.BsmtFinSF1)
plt.title('Normality of BsmtFinSF1')
Text(0.5, 1.0, 'Normality of BsmtFinSF1')
sns.countplot(data.BsmtFinSF1)
plt.title('count plot of BsmtFinSF1')
Text(0.5, 1.0, 'count plot of BsmtFinSF1')
sns.boxplot(data.BsmtFinSF1, color = 'g')
plt.title('BoxPlot Of BsmtFinSF1')
Text(0.5, 1.0, 'BoxPlot Of BsmtFinSF1')
np.corrcoef(data.BsmtFinSF2, data.Property_Sale_Price)
array([[ 1. , -0.02588584],
[-0.02588584, 1. ]])
Not a Good Predictor
data.BsmtUnfSF.isnull().sum()
0
data.BsmtUnfSF.describe()
count 2073.000000 mean 565.101302 std 449.730940 min 0.000000 25% 208.000000 50% 466.000000 75% 804.000000 max 2336.000000 Name: BsmtUnfSF, dtype: float64
data.BsmtUnfSF.value_counts()
0 173
300 11
728 11
600 11
384 10
...
730 1
251 1
241 1
744 1
1969 1
Name: BsmtUnfSF, Length: 780, dtype: int64
data.BsmtUnfSF.value_counts().sum()
2073
plt.hist(data.BsmtUnfSF)
plt.title('Hist of BsmtUnfSF')
Text(0.5, 1.0, 'Hist of BsmtUnfSF')
sns.distplot(data.BsmtUnfSF)
plt.title('Normality of BsmtUnfSF')
Text(0.5, 1.0, 'Normality of BsmtUnfSF')
sns.countplot(data.BsmtUnfSF)
plt.title('count plot of BsmtUnfSF')
Text(0.5, 1.0, 'count plot of BsmtUnfSF')
sns.boxplot(data.BsmtUnfSF, color = 'red')
plt.title('BoxPlot Of BsmtUnfSF')
Text(0.5, 1.0, 'BoxPlot Of BsmtUnfSF')
IQR = data.BsmtUnfSF.quantile(0.75) - data.BsmtUnfSF.quantile(0.25)
IQR
596.0
upper_limit = data.BsmtUnfSF.quantile(0.75) + (1.5*IQR)
upper_limit
1698.0
lower_limit = data.BsmtUnfSF.quantile(0.25) - (1.5*IQR)
lower_limit
-686.0
len(data.BsmtUnfSF[data.BsmtUnfSF > upper_limit])
41
len(data.BsmtUnfSF[data.BsmtUnfSF < lower_limit])
0
for i in np.arange(upper_limit,2700,300):
outliers = len(data.BsmtUnfSF[data.BsmtUnfSF > i])
print('At the point of: ', i, 'There are ', outliers, 'Outliers')
At the point of: 1698.0 There are 41 Outliers At the point of: 1998.0 There are 6 Outliers At the point of: 2298.0 There are 1 Outliers At the point of: 2598.0 There are 0 Outliers
data.BsmtUnfSF = np.where(data.BsmtUnfSF > upper_limit, upper_limit, data.BsmtUnfSF)
sns.boxplot(data.BsmtUnfSF, color = 'g')
plt.title('BoxPlot Of BsmtUnfSF')
Text(0.5, 1.0, 'BoxPlot Of BsmtUnfSF')
np.corrcoef(data.BsmtUnfSF, data.Property_Sale_Price)
array([[1. , 0.23819431],
[0.23819431, 1. ]])
Not a Good Predictor
data.TotalBsmtSF.isnull().sum()
0
data.TotalBsmtSF.describe()
count 2073.000000 mean 1052.270140 std 431.026436 min 0.000000 25% 794.000000 50% 990.000000 75% 1291.000000 max 6110.000000 Name: TotalBsmtSF, dtype: float64
data.TotalBsmtSF.value_counts()
0 59
864 48
816 21
912 21
672 21
..
2046 1
2042 1
6110 1
2006 1
2035 1
Name: TotalBsmtSF, Length: 721, dtype: int64
data.TotalBsmtSF.value_counts().sum()
2073
plt.hist(data.TotalBsmtSF)
plt.title('Hist of TotalBsmtSF')
Text(0.5, 1.0, 'Hist of TotalBsmtSF')
sns.distplot(data.TotalBsmtSF)
plt.title('Normality of TotalBsmtSF')
Text(0.5, 1.0, 'Normality of TotalBsmtSF')
sns.countplot(data.TotalBsmtSF)
plt.title('count plot of TotalBsmtSF')
Text(0.5, 1.0, 'count plot of TotalBsmtSF')
sns.boxplot(data.TotalBsmtSF, color = 'red')
plt.title('BoxPlot Of TotalBsmtSF')
Text(0.5, 1.0, 'BoxPlot Of TotalBsmtSF')
IQR = data.TotalBsmtSF.quantile(0.75) - data.TotalBsmtSF.quantile(0.25)
IQR
497.0
upper_limit = data.TotalBsmtSF.quantile(0.75) + (1.5*IQR)
upper_limit
2036.5
lower_limit = data.TotalBsmtSF.quantile(0.25) - (1.5*IQR)
lower_limit
48.5
len(data.TotalBsmtSF[data.TotalBsmtSF > upper_limit])
30
len(data.TotalBsmtSF[data.TotalBsmtSF < lower_limit])
59
for i in np.arange(upper_limit, 3000, 350):
outliers = len(data.TotalBsmtSF[data.TotalBsmtSF > i])
print('At the point of: ', i, 'There are', outliers, 'Outliers')
At the point of: 2036.5 There are 30 Outliers At the point of: 2386.5 There are 11 Outliers At the point of: 2736.5 There are 5 Outliers
data.TotalBsmtSF = np.where(data.TotalBsmtSF > upper_limit, upper_limit, data.TotalBsmtSF)
data.TotalBsmtSF = np.where(data.TotalBsmtSF < lower_limit, lower_limit, data.TotalBsmtSF)
sns.boxplot(data.TotalBsmtSF, color = 'g')
plt.title('BoxPlot Of TotalBsmtSF')
Text(0.5, 1.0, 'BoxPlot Of TotalBsmtSF')
np.corrcoef(data.TotalBsmtSF, data.Property_Sale_Price)
array([[1. , 0.64253607],
[0.64253607, 1. ]])
sns.scatterplot(x = data['TotalBsmtSF'], y = data.Property_Sale_Price, data = data )
plt.xlabel('TotalBsmtSF')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of TotalBsmtSF vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter plot of TotalBsmtSF vs Property_Sale_Price')
Good Predictor
data.Heating.isnull().sum()
0
data.Heating.value_counts()
GasA 2025 GasW 25 Grav 10 Wall 8 Floor 3 OthW 2 Name: Heating, dtype: int64
data.Heating.value_counts().sum()
2073
sns.countplot(data.Heating)
plt.title('Count plot of Heating')
Text(0.5, 1.0, 'Count plot of Heating')
data.groupby("Heating")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Heating | ||||||||
| Floor | 3.0 | 72500.00000 | 0.000000 | 72500.0 | 72500.0 | 72500.0 | 72500.0 | 72500.0 |
| GasA | 2025.0 | 178906.68642 | 66531.528754 | 34900.0 | 131500.0 | 165000.0 | 214900.0 | 340000.0 |
| GasW | 25.0 | 157370.32000 | 70285.099908 | 82000.0 | 115000.0 | 133900.0 | 205000.0 | 340000.0 |
| Grav | 10.0 | 72370.00000 | 32534.701132 | 37900.0 | 41550.0 | 69500.0 | 92375.0 | 121000.0 |
| OthW | 2.0 | 125750.00000 | 5303.300859 | 122000.0 | 123875.0 | 125750.0 | 127625.0 | 129500.0 |
| Wall | 8.0 | 92050.00000 | 3946.788930 | 87500.0 | 90000.0 | 90000.0 | 94175.0 | 98000.0 |
mod = ols('Property_Sale_Price ~ Heating', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Heating 5.0 2.214873e+11 4.429745e+10 10.075859 1.514722e-09 Residual 2067.0 9.087347e+12 4.396394e+09 NaN NaN
t_Heating = pairwise_tukeyhsd(data.Property_Sale_Price, data.Heating, alpha = 0.05)
print(t_Heating)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- Floor GasA 106406.6864 0.0616 -2866.2347 215679.6076 False Floor GasW 84870.32 0.2903 -30687.7029 200428.3429 False Floor Grav -130.0 0.9 -124628.1126 124368.1126 False Floor OthW 53250.0 0.9 -119397.8188 225897.8188 False Floor Wall 19550.0 0.9 -108489.0492 147589.0492 False GasA GasW -21536.3664 0.5768 -59594.3813 16521.6484 False GasA Grav -106536.6864 0.001 -166491.1348 -46582.238 True GasA OthW -53156.6864 0.8544 -186955.1362 80641.7634 False GasA Wall -86856.6864 0.0031 -153854.8503 -19858.5225 True GasW Grav -85000.32 0.0082 -155764.8679 -14235.7721 True GasW OthW -31620.32 0.9 -170599.1332 107358.4932 False GasW Wall -65320.32 0.1479 -142143.7495 11503.1095 False Grav OthW 53380.0 0.9 -93116.5321 199876.5321 False Grav Wall 19680.0 0.9 -70030.4382 109390.4382 False OthW Wall -33700.0 0.9 -183217.397 115817.397 False -----------------------------------------------------------------
data["Heating"].replace('Floor', 'Grav',inplace = True)
data["Heating"].replace('OthW', 'Grav',inplace = True)
data["Heating"].replace('Wall', 'Grav',inplace = True)
data["Heating"].replace('GasW', 'GasA',inplace = True)
data.Heating.value_counts()
GasA 2050 Grav 23 Name: Heating, dtype: int64
data.Heating.value_counts().sum()
2073
df1=data[data.Heating=='GasA']
df2=data[data.Heating=='Grav']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=6.816687704976715, pvalue=1.2177323085835995e-11)
sns.countplot(data.Heating)
plt.title('count plot of Heating')
Text(0.5, 1.0, 'count plot of Heating')
sns.catplot('Heating', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Heating vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of Heating vs Property_Sale_Price')
sns.boxplot('Heating', 'Property_Sale_Price', data=data)
plt.title('Box plot of Heating vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of Heating vs Property_Sale_Price')
Good Predictor
data.HeatingQC.isnull().sum()
0
data.HeatingQC.value_counts()
Ex 1045 TA 618 Gd 341 Fa 68 Po 1 Name: HeatingQC, dtype: int64
data.HeatingQC.value_counts().sum()
2073
sns.countplot(data.HeatingQC)
plt.title('Count plot of HeatingQC')
Text(0.5, 1.0, 'Count plot of HeatingQC')
data.groupby("HeatingQC")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| HeatingQC | ||||||||
| Ex | 1045.0 | 208654.001914 | 67436.100850 | 66500.0 | 157000.0 | 195000.0 | 253000.0 | 340000.0 |
| Fa | 68.0 | 120657.500000 | 48828.262281 | 37900.0 | 83000.0 | 121500.0 | 160000.0 | 235000.0 |
| Gd | 341.0 | 158854.217009 | 52355.542116 | 52000.0 | 125500.0 | 153500.0 | 179400.0 | 340000.0 |
| Po | 1.0 | 87000.000000 | NaN | 87000.0 | 87000.0 | 87000.0 | 87000.0 | 87000.0 |
| TA | 618.0 | 141820.336570 | 46287.982203 | 34900.0 | 115000.0 | 135000.0 | 157900.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ HeatingQC', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) HeatingQC 4.0 2.147424e+12 5.368561e+11 155.02791 4.074768e-116 Residual 2068.0 7.161410e+12 3.462964e+09 NaN NaN
t_HeatingQC = pairwise_tukeyhsd(data.Property_Sale_Price, data.HeatingQC, alpha = 0.05)
print(t_HeatingQC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
Ex Fa -87996.5019 0.001 -108103.5781 -67889.4258 True
Ex Gd -49799.7849 0.001 -59819.6119 -39779.9579 True
Ex Po -121654.0019 0.2353 -282393.1414 39085.1376 False
Ex TA -66833.6653 0.001 -74986.4822 -58680.8485 True
Fa Gd 38196.717 0.001 16859.1966 59534.2375 True
Fa Po -33657.5 0.9 -195496.8148 128181.8148 False
Fa TA 21162.8366 0.0395 635.7504 41689.9228 True
Gd Po -71854.217 0.7134 -232751.9057 89043.4717 False
Gd TA -17033.8804 0.001 -27871.9471 -6195.8138 True
Po TA 54820.3366 0.877 -105971.8825 215612.5557 False
-----------------------------------------------------------------
data["HeatingQC"].replace('Po', 'Fa',inplace = True)
data.HeatingQC.value_counts()
Ex 1045 TA 618 Gd 341 Fa 69 Name: HeatingQC, dtype: int64
data.HeatingQC.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ HeatingQC', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) HeatingQC 3.0 2.146308e+12 7.154360e+11 206.664102 3.061274e-117 Residual 2069.0 7.162526e+12 3.461830e+09 NaN NaN
t_HeatingQC = pairwise_tukeyhsd(data.Property_Sale_Price, data.HeatingQC, alpha = 0.05)
print(t_HeatingQC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------
Ex Fa -88484.2918 0.001 -107287.6653 -69680.9182 True
Ex Gd -49799.7849 0.001 -59234.3557 -40365.2141 True
Ex TA -66833.6653 0.001 -74510.2777 -59157.053 True
Fa Gd 38684.5069 0.001 18715.0588 58653.9549 True
Fa TA 21650.6264 0.0198 2449.1133 40852.1396 True
Gd TA -17033.8804 0.001 -27238.8977 -6828.8631 True
----------------------------------------------------------------
sns.countplot(data.HeatingQC)
plt.title('Count plot of HeatingQC')
Text(0.5, 1.0, 'Count plot of HeatingQC')
sns.catplot('HeatingQC', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of HeatingQC vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of HeatingQC vs Property_Sale_Price')
sns.boxplot('HeatingQC', 'Property_Sale_Price', data=data)
plt.title('Box Plot of HeatingQC vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of HeatingQC vs Property_Sale_Price')
Good Predictor
data.CentralAir.isnull().sum()
0
data.CentralAir.value_counts()
Y 1935 N 138 Name: CentralAir, dtype: int64
data.CentralAir.value_counts().sum()
2073
sns.countplot(data.CentralAir)
plt.title('Count plot of CentralAir')
Text(0.5, 1.0, 'Count plot of CentralAir')
data.groupby("CentralAir")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| CentralAir | ||||||||
| N | 138.0 | 106078.898551 | 40909.189004 | 34900.0 | 84000.0 | 100000.0 | 128750.0 | 265979.0 |
| Y | 1935.0 | 182692.770026 | 65603.012258 | 52000.0 | 135000.0 | 169990.0 | 219355.0 | 340000.0 |
df1=data[data.CentralAir=='Y']
df2=data[data.CentralAir=='N']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=13.530853321902182, pvalue=4.904316035430789e-40)
sns.catplot('CentralAir', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of CentralAir vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of CentralAir vs Property_Sale_Price')
sns.boxplot('CentralAir', 'Property_Sale_Price', data=data)
plt.title('Box Plot of CentralAir vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of CentralAir vs Property_Sale_Price')
Good Predictor
data.Electrical.isnull().sum()
1
data.Electrical.dtype
dtype('O')
data.Electrical.mode()
0 SBrkr dtype: object
data.Electrical.fillna(data.Electrical.mode()[0], inplace = True)
data.Electrical.isnull().sum()
0
data.Electrical.value_counts()
SBrkr 1902 FuseA 127 FuseF 40 FuseP 3 Mix 1 Name: Electrical, dtype: int64
data.Electrical.value_counts().sum()
2073
sns.countplot(data.Electrical)
plt.title('Count plot of Electrical')
Text(0.5, 1.0, 'Count plot of Electrical')
data.groupby("Electrical")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Electrical | ||||||||
| FuseA | 127.0 | 122764.409449 | 35751.012402 | 34900.0 | 103000.0 | 130000.0 | 142125.0 | 239000.0 |
| FuseF | 40.0 | 108434.675000 | 29055.117416 | 39300.0 | 90000.0 | 105750.0 | 129925.0 | 169500.0 |
| FuseP | 3.0 | 97333.333333 | 34645.827070 | 73000.0 | 77500.0 | 82000.0 | 109500.0 | 137000.0 |
| Mix | 1.0 | 67000.000000 | NaN | 67000.0 | 67000.0 | 67000.0 | 67000.0 | 67000.0 |
| SBrkr | 1902.0 | 182892.708202 | 66688.361340 | 37900.0 | 134108.0 | 171000.0 | 221000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ Electrical', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Electrical 4.0 6.580760e+11 1.645190e+11 39.32896 8.832416e-32 Residual 2068.0 8.650758e+12 4.183152e+09 NaN NaN
t_B = pairwise_tukeyhsd(data.Property_Sale_Price, data.Electrical, alpha = 0.05)
print(t_B)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================ group1 group2 meandiff p-adj lower upper reject ---------------------------------------------------------------- FuseA FuseF -14329.7344 0.7119 -46345.8134 17686.3445 False FuseA FuseP -25431.0761 0.9 -128576.7343 77714.5821 False FuseA Mix -55764.4094 0.9 -233038.3455 121509.5266 False FuseA SBrkr 60128.2988 0.001 43944.6788 76311.9187 True FuseF FuseP -11101.3417 0.9 -116803.8682 94601.1849 False FuseF Mix -41434.675 0.9 -220208.4027 137339.0527 False FuseF SBrkr 74458.0332 0.001 46246.2119 102669.8545 True FuseP Mix -30333.3333 0.9 -234230.4721 173563.8055 False FuseP SBrkr 85559.3749 0.1486 -16469.5639 187588.3137 False Mix SBrkr 115892.7082 0.3799 -60733.8072 292519.2237 False ----------------------------------------------------------------
data["Electrical"].replace('FuseP', 'FuseA',inplace = True)
data["Electrical"].replace('Mix', 'FuseA',inplace = True)
data["Electrical"].replace('FuseF', 'FuseA',inplace = True)
data.Electrical.value_counts()
SBrkr 1902 FuseA 171 Name: Electrical, dtype: int64
data.Electrical.value_counts().sum()
2073
df1=data[data.Electrical=='SBrkr']
df2=data[data.Electrical=='FuseA']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=12.445066649361097, pvalue=2.4338317708885697e-34)
sns.countplot(data.Electrical)
plt.title('Count plot of Electrical')
Text(0.5, 1.0, 'Count plot of Electrical')
sns.catplot('Electrical', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of Electrical vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Electrical vs Property_Sale_Price')
sns.boxplot('Electrical', 'Property_Sale_Price', data=data)
plt.title('Box Plot of Electrical vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Electrical vs Property_Sale_Price')
Good Predictor
data.FirstFlrSF.isnull().sum()
0
data.FirstFlrSF.value_counts()
864 33
1040 23
912 19
894 17
848 16
..
1898 1
1888 1
1872 1
1856 1
1987 1
Name: FirstFlrSF, Length: 753, dtype: int64
data.FirstFlrSF.value_counts().sum()
2073
plt.hist(data.FirstFlrSF)
plt.title('Hist of FirstFlrSF')
Text(0.5, 1.0, 'Hist of FirstFlrSF')
sns.distplot(data.FirstFlrSF)
plt.title('Normality of FirstFlrSF')
Text(0.5, 1.0, 'Normality of FirstFlrSF')
sns.countplot(data.FirstFlrSF)
plt.title('count plot of FirstFlrSF')
Text(0.5, 1.0, 'count plot of FirstFlrSF')
sns.boxplot(data['FirstFlrSF'], color = 'red')
plt.title('BoxPlot Of FirstFlrSF')
Text(0.5, 1.0, 'BoxPlot Of FirstFlrSF')
IQR = data['FirstFlrSF'].quantile(0.75) - data['FirstFlrSF'].quantile(0.25)
IQR
497.0
upper_limit = data['FirstFlrSF'].quantile(0.75) + (1.5*IQR)
upper_limit
2136.5
len(data['FirstFlrSF'][data['FirstFlrSF'] > upper_limit])
26
for i in np.arange(upper_limit, 3000, 500):
outliers = len(data['FirstFlrSF'][data['FirstFlrSF'] > i])
print('At the point of: ', i, 'There are ', outliers, 'outliers' )
At the point of: 2136.5 There are 26 outliers At the point of: 2636.5 There are 5 outliers
data['FirstFlrSF'] = np.where(data['FirstFlrSF'] > upper_limit, upper_limit, data['FirstFlrSF'])
sns.boxplot(data['FirstFlrSF'], color = 'g')
plt.title('BoxPlot Of FirstFlrSF')
Text(0.5, 1.0, 'BoxPlot Of FirstFlrSF')
sns.distplot(data['FirstFlrSF'])
plt.title('Normality of FirstFlrSF')
Text(0.5, 1.0, 'Normality of FirstFlrSF')
np.corrcoef(data['FirstFlrSF'], data.Property_Sale_Price)
array([[1. , 0.60991825],
[0.60991825, 1. ]])
sns.scatterplot('FirstFlrSF', 'Property_Sale_Price', data=data)
<AxesSubplot:xlabel='FirstFlrSF', ylabel='Property_Sale_Price'>
Good Predictor
data['SecondFlrSF'].isnull().sum()
0
data['SecondFlrSF'].describe()
count 2073.000000 mean 344.745779 std 438.844748 min 0.000000 25% 0.000000 50% 0.000000 75% 730.000000 max 2065.000000 Name: SecondFlrSF, dtype: float64
data['SecondFlrSF'].value_counts()
0 1195
546 13
728 11
896 10
504 10
...
1230 1
1242 1
1274 1
1286 1
1611 1
Name: SecondFlrSF, Length: 417, dtype: int64
data['SecondFlrSF'].value_counts().sum()
2073
plt.hist(data['SecondFlrSF'])
plt.title('Hist of SecondFlrSF')
Text(0.5, 1.0, 'Hist of SecondFlrSF')
sns.distplot(data['SecondFlrSF'])
plt.title('Mormality of SecondFlrSF')
Text(0.5, 1.0, 'Mormality of SecondFlrSF')
sns.countplot(data['SecondFlrSF'])
plt.title('Count plot of SecondFlrSF')
Text(0.5, 1.0, 'Count plot of SecondFlrSF')
sns.boxplot(data['SecondFlrSF'], color = 'red')
plt.title('BoxPlot Of SecondFlrSF')
Text(0.5, 1.0, 'BoxPlot Of SecondFlrSF')
IQR = data['SecondFlrSF'].quantile(0.75) - data['SecondFlrSF'].quantile(0.25)
IQR
730.0
upper_limit = data['SecondFlrSF'].quantile(0.75) + (1.5*IQR)
upper_limit
1825.0
lower_limit = data['SecondFlrSF'].quantile(0.25) - (1.5*IQR)
lower_limit
-1095.0
len(data['SecondFlrSF'][data['SecondFlrSF'] > upper_limit])
2
len(data['SecondFlrSF'][data['SecondFlrSF'] < lower_limit])
0
for i in np.arange(upper_limit, 2500, 518):
outliers = len(data['SecondFlrSF'][data['SecondFlrSF'] > i])
print('At the point of: ', i, 'There are', outliers, 'outliers')
At the point of: 1825.0 There are 2 outliers At the point of: 2343.0 There are 0 outliers
data['SecondFlrSF'] = np.where(data['SecondFlrSF'] > upper_limit, upper_limit, data['SecondFlrSF'])
sns.boxplot(data['SecondFlrSF'], color = 'g')
plt.title('BoxPlot Of SecondFlrSF')
Text(0.5, 1.0, 'BoxPlot Of SecondFlrSF')
sns.distplot(data['SecondFlrSF'])
plt.title('Mormality of SecondFlrSF')
Text(0.5, 1.0, 'Mormality of SecondFlrSF')
np.corrcoef(data['SecondFlrSF'], data.Property_Sale_Price)
array([[1. , 0.33383669],
[0.33383669, 1. ]])
sns.scatterplot('SecondFlrSF', 'Property_Sale_Price', data=data)
<AxesSubplot:xlabel='SecondFlrSF', ylabel='Property_Sale_Price'>
Not Good Predictor
data.LowQualFinSF.isnull().sum()
0
data.LowQualFinSF.describe()
count 2073.000000 mean 6.168355 std 49.553967 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 572.000000 Name: LowQualFinSF, dtype: float64
data.LowQualFinSF.value_counts()
0 2034 360 6 80 3 120 3 513 2 481 2 397 2 53 2 514 2 384 2 232 2 390 1 392 1 420 1 528 1 572 1 234 1 205 1 371 1 473 1 479 1 156 1 144 1 515 1 Name: LowQualFinSF, dtype: int64
data.LowQualFinSF.value_counts().sum()
2073
plt.hist(data.LowQualFinSF)
plt.title('Hist of LowQualFinSF')
Text(0.5, 1.0, 'Hist of LowQualFinSF')
sns.distplot(data.LowQualFinSF)
plt.title('Normality of LowQualFinSF')
Text(0.5, 1.0, 'Normality of LowQualFinSF')
sns.countplot(data.LowQualFinSF)
plt.title('Count plot of LowQualFinSF')
Text(0.5, 1.0, 'Count plot of LowQualFinSF')
sns.boxplot(data.LowQualFinSF, color = 'red')
plt.title('BoxPlot Of LowQualFinSF')
Text(0.5, 1.0, 'BoxPlot Of LowQualFinSF')
IQR = data['LowQualFinSF'].quantile(0.75) - data['LowQualFinSF'].quantile(0.25)
IQR
0.0
upper_limit = data['LowQualFinSF'].quantile(0.75) + (1.5*IQR)
upper_limit
0.0
lower_limit = data['LowQualFinSF'].quantile(0.25) - (1.5*IQR)
lower_limit
0.0
Not a Good Predictor
data.GrLivArea.isnull().sum()
0
data.GrLivArea.describe()
count 2073.000000 mean 1517.011095 std 517.236428 min 334.000000 25% 1126.000000 50% 1470.000000 75% 1792.000000 max 5642.000000 Name: GrLivArea, dtype: float64
data.GrLivArea.value_counts()
864 28
1040 20
894 16
848 13
1200 12
..
2704 1
790 1
2668 1
2654 1
2035 1
Name: GrLivArea, Length: 861, dtype: int64
data.GrLivArea.value_counts().sum()
2073
plt.hist(data.GrLivArea)
plt.title('Hit of GrLivArea')
Text(0.5, 1.0, 'Hit of GrLivArea')
sns.distplot(data.GrLivArea)
plt.title('Normality of GrLivArea')
Text(0.5, 1.0, 'Normality of GrLivArea')
sns.countplot(data.GrLivArea)
plt.title('Count plot of GrLivArea')
Text(0.5, 1.0, 'Count plot of GrLivArea')
sns.boxplot(data.GrLivArea, color = 'red')
plt.title('BoxPlot Of GrLivArea')
Text(0.5, 1.0, 'BoxPlot Of GrLivArea')
IQR = data.GrLivArea.quantile(0.75)- data.GrLivArea.quantile(0.25)
IQR
666.0
upper_limit = data.GrLivArea.quantile(0.75) + (1.5*IQR)
upper_limit
2791.0
lower_limit = data.GrLivArea.quantile(0.25) -(1.5*IQR)
lower_limit
127.0
len(data.GrLivArea[data.GrLivArea > upper_limit])
39
len(data.GrLivArea[data.GrLivArea < lower_limit])
0
for i in np.arange(upper_limit, 4800, 500):
outliers = len(data.GrLivArea[data.GrLivArea > i])
print('At the point of: ', i, 'There are', outliers, 'outliers')
At the point of: 2791.0 There are 39 outliers At the point of: 3291.0 There are 9 outliers At the point of: 3791.0 There are 4 outliers At the point of: 4291.0 There are 4 outliers At the point of: 4791.0 There are 1 outliers
data.GrLivArea = np.where(data.GrLivArea > upper_limit, upper_limit, data.GrLivArea)
sns.boxplot(data.GrLivArea, color = 'g')
plt.title('BoxPlot Of GrLivArea')
Text(0.5, 1.0, 'BoxPlot Of GrLivArea')
sns.distplot(data.GrLivArea)
plt.title('Normality of GrLivArea')
Text(0.5, 1.0, 'Normality of GrLivArea')
np.corrcoef(data.GrLivArea, data.Property_Sale_Price)
array([[1. , 0.73086878],
[0.73086878, 1. ]])
sns.scatterplot(data.GrLivArea, data.Property_Sale_Price)
plt.title('GrLivArea vs Property_Sale_Price')
Text(0.5, 1.0, 'GrLivArea vs Property_Sale_Price')
Good Predictor
data.BsmtFullBath.isnull().sum()
0
data.BsmtFullBath.describe()
count 2073.000000 mean 0.429329 std 0.522605 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 3.000000 Name: BsmtFullBath, dtype: float64
data.BsmtFullBath.value_counts()
0 1210 1 838 2 23 3 2 Name: BsmtFullBath, dtype: int64
data.BsmtFullBath.value_counts().sum()
2073
sns.countplot(data.BsmtFullBath)
plt.title('Count plot of BsmtFullBath')
Text(0.5, 1.0, 'Count plot of BsmtFullBath')
data.groupby("BsmtFullBath")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BsmtFullBath | ||||||||
| 0 | 1210.0 | 164453.647934 | 59900.550784 | 34900.0 | 120625.0 | 155417.5 | 193969.75 | 340000.0 |
| 1 | 838.0 | 195526.536993 | 71769.877578 | 35311.0 | 140000.0 | 176750.0 | 240000.00 | 340000.0 |
| 2 | 23.0 | 215271.565217 | 82149.958655 | 108959.0 | 147950.0 | 188000.0 | 292000.00 | 340000.0 |
| 3 | 2.0 | 179000.000000 | 0.000000 | 179000.0 | 179000.0 | 179000.0 | 179000.00 | 179000.0 |
mod = ols('Property_Sale_Price ~ BsmtFullBath', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtFullBath 1.0 4.969889e+11 4.969889e+11 116.804584 1.592558e-26 Residual 2071.0 8.811845e+12 4.254875e+09 NaN NaN
t_BsmtFullBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFullBath)
print(t_BsmtFullBath)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------
0 1 31072.8891 0.001 23537.9572 38607.8209 True
0 2 50817.9173 0.0013 15527.7285 86108.1061 True
0 3 14546.3521 0.9 -104104.9952 133197.6993 False
1 2 19745.0282 0.4798 -15690.972 55181.0284 False
1 3 -16526.537 0.9 -135221.3342 102168.2602 False
2 3 -36271.5652 0.8617 -159872.039 87328.9085 False
----------------------------------------------------------------
data["BsmtFullBath"].replace(0, 3,inplace = True)
data["BsmtFullBath"].replace(2, 1,inplace = True)
data.BsmtFullBath.value_counts()
3 1212 1 861 Name: BsmtFullBath, dtype: int64
data.BsmtFullBath.value_counts().sum()
2073
df1=data[data.BsmtFullBath==3]
df2=data[data.BsmtFullBath==1]
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=-10.864081506501448, pvalue=8.882352183807123e-27)
sns.countplot(data.BsmtFullBath)
plt.title('Count plot of BsmtFullBath')
Text(0.5, 1.0, 'Count plot of BsmtFullBath')
sns.catplot('BsmtFullBath', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtFullBath vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat plot of BsmtFullBath vs Property_Sale_Price')
sns.boxplot('BsmtFullBath', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtFullBath vs Property_Sale_Price')
Text(0.5, 1.0, 'Box plot of BsmtFullBath vs Property_Sale_Price')
Good Predictor
data.BsmtHalfBath.isnull().sum()
0
data.BsmtHalfBath.describe()
count 2073.000000 mean 0.056440 std 0.237015 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 2.000000 Name: BsmtHalfBath, dtype: float64
data.BsmtHalfBath.value_counts()
0 1959 1 111 2 3 Name: BsmtHalfBath, dtype: int64
data.BsmtHalfBath.value_counts().sum()
2073
sns.countplot(data.BsmtHalfBath)
plt.title('Count plot of BsmtHalfBath')
Text(0.5, 1.0, 'Count plot of BsmtHalfBath')
data.groupby("BsmtHalfBath")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BsmtHalfBath | ||||||||
| 0 | 1959.0 | 178099.921899 | 67934.574168 | 34900.0 | 129500.0 | 164000.0 | 215000.0 | 340000.0 |
| 1 | 111.0 | 169391.441441 | 48541.601124 | 91500.0 | 138250.0 | 163500.0 | 177000.0 | 340000.0 |
| 2 | 3.0 | 149733.666667 | 38509.840305 | 127500.0 | 127500.0 | 127500.0 | 160850.5 | 194201.0 |
mod = ols('Property_Sale_Price ~ BsmtHalfBath', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BsmtHalfBath 1.0 9.974184e+09 9.974184e+09 2.221405 0.136261 Residual 2071.0 9.298860e+12 4.490034e+09 NaN NaN
Not Good Predictor
data.FullBath.isnull().sum()
0
data.FullBath.describe()
count 2073.000000 mean 1.571635 std 0.545963 min 0.000000 25% 1.000000 50% 2.000000 75% 2.000000 max 3.000000 Name: FullBath, dtype: float64
data.FullBath.value_counts()
2 1117 1 901 3 41 0 14 Name: FullBath, dtype: int64
data.FullBath.value_counts().sum()
2073
sns.countplot(data.FullBath)
plt.title('Count plot of FullBath')
Text(0.5, 1.0, 'Count plot of FullBath')
data.groupby("FullBath")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| FullBath | ||||||||
| 0 | 14.0 | 180887.857143 | 122234.041510 | 35311.0 | 72989.75 | 149168.5 | 320000.0 | 340000.0 |
| 1 | 901.0 | 134287.288568 | 38751.157631 | 34900.0 | 112000.00 | 132000.0 | 149500.0 | 340000.0 |
| 2 | 1117.0 | 208497.468218 | 62426.317696 | 55000.0 | 168500.00 | 195000.0 | 248000.0 | 340000.0 |
| 3 | 41.0 | 286157.292683 | 65967.887097 | 170000.0 | 225000.00 | 325000.0 | 340000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ FullBath', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) FullBath 1.0 3.046506e+12 3.046506e+12 1007.503049 1.639697e-180 Residual 2071.0 6.262328e+12 3.023818e+09 NaN NaN
t_FullBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.FullBath)
print(t_FullBath)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
===============================================================
group1 group2 meandiff p-adj lower upper reject
---------------------------------------------------------------
0 1 -46600.5686 0.0077 -84104.9235 -9096.2136 True
0 2 27609.6111 0.2303 -9839.2198 65058.442 False
0 3 105269.4355 0.001 62164.9005 148373.9706 True
1 2 74210.1797 0.001 67974.7101 80445.6492 True
1 3 151870.0041 0.001 129633.3882 174106.62 True
2 3 77659.8245 0.001 55516.984 99802.6649 True
---------------------------------------------------------------
data["FullBath"].replace(0, 2,inplace = True)
data.FullBath.value_counts()
2 1131 1 901 3 41 Name: FullBath, dtype: int64
data.FullBath.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ FullBath', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) FullBath 1.0 3.228811e+12 3.228811e+12 1099.809517 8.243835e-194 Residual 2071.0 6.080023e+12 2.935791e+09 NaN NaN
t_FullBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.FullBath)
print(t_FullBath)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==============================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------
1 2 73868.4161 0.001 68192.5205 79544.3117 True
1 3 151870.0041 0.001 131572.7461 172167.2622 True
2 3 78001.588 0.001 57794.3563 98208.8197 True
--------------------------------------------------------------
sns.countplot(data.FullBath)
plt.title('Count plot of FullBath')
Text(0.5, 1.0, 'Count plot of FullBath')
sns.catplot('FullBath', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of FullBath vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of FullBath vs Property_Sale_Price')
sns.boxplot('FullBath', 'Property_Sale_Price', data=data)
plt.title('Box Plot of FullBath vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of FullBath vs Property_Sale_Price')
Good Predictor
data.HalfBath.isnull().sum()
0
data.HalfBath.describe()
count 2073.000000 mean 0.378678 std 0.499873 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 2.000000 Name: HalfBath, dtype: float64
data.HalfBath.value_counts()
0 1303 1 755 2 15 Name: HalfBath, dtype: int64
data.HalfBath.value_counts().sum()
2073
sns.countplot(data.HalfBath)
plt.title('Count plot of HalfBath')
Text(0.5, 1.0, 'Count plot of HalfBath')
data.groupby("HalfBath")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| HalfBath | ||||||||
| 0 | 1303.0 | 160961.395242 | 61982.511951 | 34900.0 | 119200.0 | 143000.0 | 189000.0 | 340000.0 |
| 1 | 755.0 | 206681.527152 | 66063.562011 | 61000.0 | 159250.0 | 190000.0 | 246750.0 | 340000.0 |
| 2 | 15.0 | 158143.133333 | 28133.306194 | 108959.0 | 137500.0 | 145900.0 | 182500.0 | 201800.0 |
mod = ols('Property_Sale_Price ~ HalfBath', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) HalfBath 1.0 8.827791e+11 8.827791e+11 216.974087 8.835252e-47 Residual 2071.0 8.426055e+12 4.068592e+09 NaN NaN
t_HalfBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.HalfBath)
print(t_HalfBath)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==============================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------
0 1 45720.1319 0.001 38925.7075 52514.5563 True
0 2 -2818.2619 0.9 -41394.1147 35757.5909 False
1 2 -48538.3938 0.0093 -87273.248 -9803.5397 True
--------------------------------------------------------------
data["HalfBath"].replace(2, 0,inplace = True)
data.HalfBath.value_counts()
0 1318 1 755 Name: HalfBath, dtype: int64
data.HalfBath.value_counts().sum()
2073
df1=data[data.HalfBath==0]
df2=data[data.HalfBath==1]
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=-15.83031334361822, pvalue=2.276986553597032e-53)
sns.countplot(data.HalfBath)
plt.title('Count plot of HalfBath')
Text(0.5, 1.0, 'Count plot of HalfBath')
sns.catplot('HalfBath', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of HalfBath vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of HalfBath vs Property_Sale_Price')
sns.boxplot('HalfBath', 'Property_Sale_Price', data=data)
plt.title('Box Plot of HalfBath vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of HalfBath vs Property_Sale_Price')
Good Predictor
data.BedroomAbvGr.isnull().sum()
0
data.BedroomAbvGr.describe()
count 2073.000000 mean 2.874096 std 0.816144 min 0.000000 25% 2.000000 50% 3.000000 75% 3.000000 max 8.000000 Name: BedroomAbvGr, dtype: float64
data.BedroomAbvGr.value_counts()
3 1131 2 503 4 319 1 71 5 30 0 9 6 9 8 1 Name: BedroomAbvGr, dtype: int64
data.BedroomAbvGr.value_counts().sum()
2073
sns.countplot(data.BedroomAbvGr)
plt.title('Count plot of BedroomAbvGr')
Text(0.5, 1.0, 'Count plot of BedroomAbvGr')
data.groupby("BedroomAbvGr")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| BedroomAbvGr | ||||||||
| 0 | 9.0 | 255995.444444 | 97227.860908 | 108959.0 | 145000.0 | 286000.0 | 340000.0 | 340000.0 |
| 1 | 71.0 | 162525.887324 | 80611.069754 | 35311.0 | 89000.0 | 149300.0 | 199900.0 | 340000.0 |
| 2 | 503.0 | 154924.240557 | 65888.675106 | 34900.0 | 110000.0 | 138000.0 | 185000.0 | 340000.0 |
| 3 | 1131.0 | 180181.955791 | 59513.952844 | 40000.0 | 136500.0 | 172400.0 | 214000.0 | 340000.0 |
| 4 | 319.0 | 205681.056426 | 76222.288380 | 64500.0 | 139800.0 | 192140.0 | 266239.5 | 340000.0 |
| 5 | 30.0 | 182960.000000 | 70458.922246 | 81000.0 | 135250.0 | 160750.0 | 233625.0 | 320000.0 |
| 6 | 9.0 | 143595.444444 | 22913.930371 | 119000.0 | 135900.0 | 142600.0 | 142953.0 | 200000.0 |
| 8 | 1.0 | 200000.000000 | NaN | 200000.0 | 200000.0 | 200000.0 | 200000.0 | 200000.0 |
mod = ols('Property_Sale_Price ~ BedroomAbvGr', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BedroomAbvGr 1.0 2.869680e+11 2.869680e+11 65.874481 8.152662e-16 Residual 2071.0 9.021866e+12 4.356285e+09 NaN NaN
t_BedroomAbvGr = pairwise_tukeyhsd(data.Property_Sale_Price, data.BedroomAbvGr)
print(t_BedroomAbvGr)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
0 1 -93469.5571 0.0013 -163181.0252 -23758.089 True
0 2 -101071.2039 0.001 -167329.3679 -34813.0399 True
0 3 -75813.4887 0.0116 -141747.5052 -9879.4721 True
0 4 -50314.388 0.2983 -116907.6042 16278.8281 False
0 5 -73035.4444 0.0621 -147914.4531 1843.5642 False
0 6 -112400.0 0.006 -205275.9796 -19524.0204 True
0 8 -55995.4444 0.9 -263672.4483 151681.5594 False
1 2 -7601.6468 0.9 -32579.3467 17376.0531 False
1 3 17656.0685 0.3388 -6448.6144 41760.7513 False
1 4 43155.1691 0.001 17301.7818 69008.5564 True
1 5 20434.1127 0.8153 -22468.1848 63336.4101 False
1 6 -18930.4429 0.9 -88641.911 50781.0252 False
1 8 37474.1127 0.9 -160928.2037 235876.429 False
2 3 25257.7152 0.001 14698.7735 35816.657 True
2 4 50756.8159 0.001 36655.2858 64858.346 True
2 5 28035.7594 0.2956 -8992.1036 65063.6225 False
2 6 -11328.7961 0.9 -77586.9601 54929.3679 False
2 8 45075.7594 0.9 -152139.6929 242291.2118 False
3 4 25499.1006 0.001 13008.9688 37989.2324 True
3 5 2778.0442 0.9 -33666.6109 39222.6993 False
3 6 -36586.5113 0.6729 -102520.5279 29347.5052 False
3 8 19818.0442 0.9 -177288.7413 216924.8297 False
4 5 -22721.0564 0.5841 -60345.1815 14903.0686 False
4 6 -62085.612 0.0883 -128678.8281 4507.6042 False
4 8 -5681.0564 0.9 -203009.3281 191647.2152 False
5 6 -39364.5556 0.7258 -114243.5642 35514.4531 False
5 8 17040.0 0.9 -183236.4496 217316.4496 False
6 8 56404.5556 0.9 -151272.4483 264081.5594 False
-----------------------------------------------------------------
data["BedroomAbvGr"].replace(8, 0,inplace = True)
data["BedroomAbvGr"].replace(1, 2,inplace = True)
data["BedroomAbvGr"].replace(6, 2,inplace = True)
data["BedroomAbvGr"].replace(5, 3,inplace = True)
data["BedroomAbvGr"].replace(0, 4,inplace = True)
data.BedroomAbvGr.value_counts()
3 1161 2 583 4 329 Name: BedroomAbvGr, dtype: int64
data.BedroomAbvGr.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ BedroomAbvGr', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) BedroomAbvGr 1.0 5.729822e+11 5.729822e+11 135.836339 1.890442e-30 Residual 2071.0 8.735852e+12 4.218181e+09 NaN NaN
t_BedroomAbvGr = pairwise_tukeyhsd(data.Property_Sale_Price, data.BedroomAbvGr)
print(t_BedroomAbvGr)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
===========================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------
2 3 24578.6284 0.001 16844.7854 32312.4713 True
2 4 51365.0587 0.001 40859.0517 61871.0658 True
3 4 26786.4303 0.001 17270.4931 36302.3676 True
-----------------------------------------------------------
sns.countplot(data.BedroomAbvGr)
plt.title('Count plot of BedroomAbvGr')
Text(0.5, 1.0, 'Count plot of BedroomAbvGr')
sns.catplot('BedroomAbvGr', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of BedroomAbvGr vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of BedroomAbvGr vs Property_Sale_Price')
sns.boxplot('BedroomAbvGr', 'Property_Sale_Price', data=data)
plt.title('Box Plot of BedroomAbvGr vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of BedroomAbvGr vs Property_Sale_Price')
Good Predictor
data.KitchenAbvGr.isnull().sum()
0
data.KitchenAbvGr.describe()
count 2073.000000 mean 1.052581 std 0.237901 min 0.000000 25% 1.000000 50% 1.000000 75% 1.000000 max 3.000000 Name: KitchenAbvGr, dtype: float64
data.KitchenAbvGr.value_counts()
1 1965 2 101 3 5 0 2 Name: KitchenAbvGr, dtype: int64
data.KitchenAbvGr.value_counts().sum()
2073
sns.countplot(data.KitchenAbvGr)
plt.title('Count plot of KitchenAbvGr')
Text(0.5, 1.0, 'Count plot of KitchenAbvGr')
data.groupby("KitchenAbvGr")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| KitchenAbvGr | ||||||||
| 0 | 2.0 | 127500.000000 | 0.000000 | 127500.0 | 127500.0 | 127500.0 | 127500.0 | 127500.0 |
| 1 | 1965.0 | 180190.105344 | 67589.787291 | 34900.0 | 132000.0 | 168000.0 | 217000.0 | 340000.0 |
| 2 | 101.0 | 131315.257426 | 28309.983888 | 55000.0 | 112000.0 | 136000.0 | 145000.0 | 206300.0 |
| 3 | 5.0 | 111600.000000 | 3130.495168 | 106000.0 | 113000.0 | 113000.0 | 113000.0 | 113000.0 |
mod = ols('Property_Sale_Price ~ KitchenAbvGr', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) KitchenAbvGr 1.0 2.335843e+11 2.335843e+11 53.304667 4.052531e-13 Residual 2071.0 9.075250e+12 4.382062e+09 NaN NaN
t_KitchenAbvGr = pairwise_tukeyhsd(data.Property_Sale_Price, data.KitchenAbvGr)
print(t_KitchenAbvGr)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------
0 1 52690.1053 0.6532 -67628.4171 173008.6278 False
0 2 3815.2574 0.9 -117626.9109 125257.4258 False
0 3 -15900.0 0.9 -158190.4017 126390.4017 False
1 2 -48874.8479 0.001 -66226.8575 -31522.8383 True
1 3 -68590.1053 0.0947 -144744.228 7564.0173 False
2 3 -19715.2574 0.9 -97632.5456 58202.0307 False
----------------------------------------------------------------
data["KitchenAbvGr"].replace(0, 2,inplace = True)
data["KitchenAbvGr"].replace(3, 2,inplace = True)
data.KitchenAbvGr.value_counts()
1 1965 2 108 Name: KitchenAbvGr, dtype: int64
data.KitchenAbvGr.value_counts().sum()
2073
df1=data[data.KitchenAbvGr==1]
df2=data[data.KitchenAbvGr==2]
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=7.6294265770566945, pvalue=3.571786832048337e-14)
sns.countplot(data.KitchenAbvGr)
plt.title('Count plot of KitchenAbvGr')
Text(0.5, 1.0, 'Count plot of KitchenAbvGr')
sns.catplot('KitchenAbvGr', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of KitchenAbvGr vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of KitchenAbvGr vs Property_Sale_Price')
sns.boxplot('KitchenAbvGr', 'Property_Sale_Price', data=data)
plt.title('Box Plot of KitchenAbvGr vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of KitchenAbvGr vs Property_Sale_Price')
Good Predictor
data.KitchenQual.isnull().sum()
0
data.KitchenQual.value_counts()
TA 1043 Gd 838 Ex 144 Fa 48 Name: KitchenQual, dtype: int64
data.KitchenQual.value_counts().sum()
2073
sns.countplot(data.KitchenQual)
plt.title('Count plot of KitchenQual')
Text(0.5, 1.0, 'Count plot of KitchenQual')
data.groupby("KitchenQual")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| KitchenQual | ||||||||
| Ex | 144.0 | 287234.361111 | 64792.366051 | 86000.0 | 261125.0 | 315500.0 | 340000.0 | 340000.0 |
| Fa | 48.0 | 104995.687500 | 33305.855391 | 39300.0 | 82000.0 | 111750.0 | 129000.0 | 200000.0 |
| Gd | 838.0 | 210808.052506 | 56926.121329 | 79000.0 | 173125.0 | 201000.0 | 245262.5 | 340000.0 |
| TA | 1043.0 | 139109.021093 | 37824.206016 | 34900.0 | 115000.0 | 136500.0 | 160000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ KitchenQual', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) KitchenQual 3.0 4.453250e+12 1.484417e+12 632.520772 9.879853e-292 Residual 2069.0 4.855584e+12 2.346827e+09 NaN NaN
t_KitchenQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.KitchenQual)
print(t_KitchenQual)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
Ex Fa -182238.6736 0.001 -202997.9522 -161479.395 True
Ex Gd -76426.3086 0.001 -87662.419 -65190.1982 True
Ex TA -148125.34 0.001 -159198.3443 -137052.3358 True
Fa Gd 105812.365 0.001 87326.5867 124298.1433 True
Fa TA 34113.3336 0.001 15726.239 52500.4282 True
Gd TA -71699.0314 0.001 -77477.247 -65920.8158 True
-----------------------------------------------------------------
sns.catplot('KitchenQual', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of KitchenQual vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of KitchenQual vs Property_Sale_Price')
sns.boxplot('KitchenQual', 'Property_Sale_Price', data=data)
plt.title('Box Plot of KitchenQual vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of KitchenQual vs Property_Sale_Price')
Good Predictor
data.TotRmsAbvGrd.isnull().sum()
0
data.TotRmsAbvGrd.describe()
count 2073.000000 mean 6.540280 std 1.629673 min 2.000000 25% 5.000000 50% 6.000000 75% 7.000000 max 14.000000 Name: TotRmsAbvGrd, dtype: float64
data.TotRmsAbvGrd.value_counts()
6 580 7 458 5 396 8 269 4 127 9 106 10 65 11 30 3 22 12 18 2 1 14 1 Name: TotRmsAbvGrd, dtype: int64
data.TotRmsAbvGrd.value_counts().sum()
2073
plt.hist(data.TotRmsAbvGrd)
plt.title('Histogram of TotRmsAbvGrd')
Text(0.5, 1.0, 'Histogram of TotRmsAbvGrd')
sns.distplot(data.TotRmsAbvGrd)
plt.title('Normality of TotRmsAbvGrd')
Text(0.5, 1.0, 'Normality of TotRmsAbvGrd')
sns.countplot(data.TotRmsAbvGrd)
plt.title('Count plot of TotRmsAbvGrd')
Text(0.5, 1.0, 'Count plot of TotRmsAbvGrd')
sns.boxplot(data.TotRmsAbvGrd, color = 'red')
plt.title('BoxPlot Of TotRmsAbvGrd')
Text(0.5, 1.0, 'BoxPlot Of TotRmsAbvGrd')
IQR = data.TotRmsAbvGrd.quantile(.75) - data.TotRmsAbvGrd.quantile(.25)
IQR
2.0
upper_limit = data.TotRmsAbvGrd.quantile(.75) + (1.5*IQR)
upper_limit
10.0
lower_limit = data.TotRmsAbvGrd.quantile(.25) - (1.5*IQR)
lower_limit
2.0
len(data.TotRmsAbvGrd[data.TotRmsAbvGrd > upper_limit])
49
len(data.TotRmsAbvGrd[data.TotRmsAbvGrd < lower_limit])
0
for i in np.arange(upper_limit, 12, 1):
outliers = len(data.TotRmsAbvGrd[data.TotRmsAbvGrd > i])
print('At the point of:', i, 'There are ', outliers)
At the point of: 10.0 There are 49 At the point of: 11.0 There are 19
data.TotRmsAbvGrd = np.where(data.TotRmsAbvGrd > upper_limit, upper_limit, data.TotRmsAbvGrd)
sns.boxplot(data.TotRmsAbvGrd, color = 'g')
plt.title('BoxPlot Of TotRmsAbvGrd')
Text(0.5, 1.0, 'BoxPlot Of TotRmsAbvGrd')
np.corrcoef(data.TotRmsAbvGrd, data.Property_Sale_Price)
array([[1. , 0.5244364],
[0.5244364, 1. ]])
sns.scatterplot(data.TotRmsAbvGrd,data.Property_Sale_Price )
<AxesSubplot:xlabel='TotRmsAbvGrd', ylabel='Property_Sale_Price'>
Not Good Predictor
data.Functional.isnull().sum()
0
data.Functional.value_counts()
Typ 1937 Min1 49 Min2 47 Mod 18 Maj1 15 Maj2 6 Sev 1 Name: Functional, dtype: int64
data.Functional.value_counts().sum()
2073
sns.countplot(data.Functional)
plt.title('Count Plot of Functional - count')
Text(0.5, 1.0, 'Count Plot of Functional - count')
data.groupby("Functional")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Functional | ||||||||
| Maj1 | 15.0 | 154018.266667 | 62157.987000 | 60000.0 | 130000.0 | 144000.0 | 177750.0 | 315000.0 |
| Maj2 | 6.0 | 85666.666667 | 24229.458654 | 52000.0 | 71500.0 | 85000.0 | 103750.0 | 115000.0 |
| Min1 | 49.0 | 144162.244898 | 38515.982249 | 82000.0 | 118000.0 | 139000.0 | 170000.0 | 256000.0 |
| Min2 | 47.0 | 143016.638298 | 37460.315266 | 87000.0 | 125500.0 | 140000.0 | 160750.0 | 316600.0 |
| Mod | 18.0 | 162550.000000 | 85663.282339 | 55000.0 | 100375.0 | 148950.0 | 197500.0 | 340000.0 |
| Sev | 1.0 | 129000.000000 | NaN | 129000.0 | 129000.0 | 129000.0 | 129000.0 | 129000.0 |
| Typ | 1937.0 | 179909.391843 | 67437.150215 | 34900.0 | 131500.0 | 167000.0 | 217000.0 | 340000.0 |
mod_GC = ols('Property_Sale_Price ~ Functional', data = data).fit()
sm.stats.anova_lm(mod_GC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| Functional | 6.0 | 1.868199e+11 | 3.113665e+10 | 7.051986 | 1.902607e-07 |
| Residual | 2066.0 | 9.122014e+12 | 4.415302e+09 | NaN | NaN |
tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.Functional, alpha = 0.05)
print(tukey_GC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================ group1 group2 meandiff p-adj lower upper reject ---------------------------------------------------------------- Maj1 Maj2 -68351.6 0.3356 -163077.2325 26374.0325 False Maj1 Min1 -9856.0218 0.9 -67722.2851 48010.2415 False Maj1 Min2 -11001.6284 0.9 -69155.7376 47152.4808 False Maj1 Mod 8531.7333 0.9 -60025.6462 77089.1128 False Maj1 Sev -25018.2667 0.9 -227550.1882 177513.6548 False Maj1 Typ 25891.1252 0.7155 -24937.5263 76719.7767 False Maj2 Min1 58495.5782 0.3943 -26322.1822 143313.3386 False Maj2 Min2 57349.9716 0.4231 -27664.4299 142364.3732 False Maj2 Mod 76883.3333 0.1765 -15559.4183 169326.085 False Maj2 Sev 43333.3333 0.9 -168479.6202 255146.2868 False Maj2 Typ 94242.7252 0.0096 14061.0573 174424.3931 True Min1 Min2 -1145.6066 0.9 -41183.1819 38891.9687 False Min1 Mod 18387.7551 0.9 -35660.5687 72436.0789 False Min1 Sev -15162.2449 0.9 -213253.8556 182929.3658 False Min1 Typ 35747.1469 0.0039 7380.6378 64113.656 True Min2 Mod 19533.3617 0.9 -34823.0298 73889.7532 False Min2 Sev -14016.6383 0.9 -212192.5254 184159.2488 False Min2 Typ 36892.7535 0.0033 7943.5769 65841.9302 True Mod Sev -33550.0 0.9 -235024.3063 167924.3063 False Mod Typ 17359.3918 0.9 -29076.2485 63795.0322 False Sev Typ 50909.3918 0.9 -145241.9111 247060.6948 False ----------------------------------------------------------------
data["Functional"].replace('Maj1', 'Min1',inplace = True)
data["Functional"].replace('Min2', 'Min1',inplace = True)
data["Functional"].replace('Mod', 'Min1',inplace = True)
data["Functional"].replace('Sev', 'Min1',inplace = True)
data.Functional.value_counts()
Typ 1937 Min1 130 Maj2 6 Name: Functional, dtype: int64
data.Functional.value_counts().sum()
2073
mod_GC = ols('Property_Sale_Price ~ Functional', data = data).fit()
sm.stats.anova_lm(mod_GC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| Functional | 2.0 | 1.802771e+11 | 9.013856e+10 | 20.439903 | 1.620241e-09 |
| Residual | 2070.0 | 9.128557e+12 | 4.409931e+09 | NaN | NaN |
tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.Functional, alpha = 0.05)
print(tukey_GC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ============================================================= group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------- Maj2 Min1 61647.9949 0.0676 -3388.6096 126684.5993 False Maj2 Typ 94242.7252 0.0015 30558.5291 157926.9212 True Min1 Typ 32594.7303 0.001 18483.3513 46706.1093 True -------------------------------------------------------------
data["Functional"].replace('Maj2', 'Min1',inplace = True)
data.Functional.value_counts()
Typ 1937 Min1 136 Name: Functional, dtype: int64
data.Functional.value_counts().sum()
2073
df1=data[data.Functional=='Typ']
df2=data[data.Functional=='Min1']
ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)
Ttest_indResult(statistic=5.989060705579733, pvalue=2.4817905193362626e-09)
sns.catplot('Functional', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of Functional vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Functional vs Property_Sale_Price')
sns.boxplot('Functional', 'Property_Sale_Price', data=data)
plt.title('Box Plot of Functional vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Functional vs Property_Sale_Price')
Good Pradictor
data.Fireplaces.isnull().sum()
0
data.Fireplaces.describe()
count 2073.000000 mean 0.602026 std 0.633931 min 0.000000 25% 0.000000 50% 1.000000 75% 1.000000 max 3.000000 Name: Fireplaces, dtype: float64
data.Fireplaces.value_counts()
0 988 1 927 2 153 3 5 Name: Fireplaces, dtype: int64
data.Fireplaces.value_counts().sum()
2073
sns.countplot(data.Fireplaces)
plt.title('Count plot of Fireplaces')
Text(0.5, 1.0, 'Count plot of Fireplaces')
data.groupby("Fireplaces")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Fireplaces | ||||||||
| 0 | 988.0 | 141606.623482 | 44270.114578 | 34900.0 | 112375.0 | 135625.0 | 165000.0 | 340000.0 |
| 1 | 927.0 | 208329.539374 | 66425.592636 | 60000.0 | 159975.0 | 190000.0 | 255250.0 | 340000.0 |
| 2 | 153.0 | 221474.320261 | 71854.986236 | 100000.0 | 160000.0 | 206900.0 | 271000.0 | 340000.0 |
| 3 | 5.0 | 247000.000000 | 86429.161745 | 160000.0 | 190000.0 | 205000.0 | 340000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ Fireplaces', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Fireplaces 1.0 2.216651e+12 2.216651e+12 647.287754 1.763214e-124 Residual 2071.0 7.092184e+12 3.424521e+09 NaN NaN
t_Fireplaces = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fireplaces)
print(t_Fireplaces)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
===============================================================
group1 group2 meandiff p-adj lower upper reject
---------------------------------------------------------------
0 1 66722.9159 0.001 59965.5961 73480.2357 True
0 2 79867.6968 0.001 67028.8191 92706.5744 True
0 3 105393.3765 0.001 39138.2367 171648.5164 True
1 2 13144.7809 0.0438 249.3838 26040.178 True
1 3 38670.4606 0.4389 -27595.6547 104936.576 False
2 3 25525.6797 0.7367 -41633.6342 92684.9937 False
---------------------------------------------------------------
data["Fireplaces"].replace(3, 2,inplace = True)
data.Fireplaces.value_counts()
0 988 1 927 2 158 Name: Fireplaces, dtype: int64
data.Fireplaces.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ Fireplaces', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) Fireplaces 1.0 2.231890e+12 2.231890e+12 653.141157 1.894439e-125 Residual 2071.0 7.076944e+12 3.417163e+09 NaN NaN
t_Fireplaces = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fireplaces)
print(t_Fireplaces)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
============================================================
group1 group2 meandiff p-adj lower upper reject
------------------------------------------------------------
0 1 66722.9159 0.001 60558.8601 72886.9717 True
0 2 80675.4715 0.001 69125.3699 92225.573 True
1 2 13952.5556 0.0134 2350.1787 25554.9325 True
------------------------------------------------------------
sns.countplot(data.Fireplaces)
plt.title('Count plot of Fireplaces')
Text(0.5, 1.0, 'Count plot of Fireplaces')
sns.catplot('Fireplaces', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of Fireplaces vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of Fireplaces vs Property_Sale_Price')
sns.boxplot('Fireplaces', 'Property_Sale_Price', data=data)
plt.title('Box Plot of Fireplaces vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of Fireplaces vs Property_Sale_Price')
Good Predictor
data.FireplaceQu.isnull().sum()
988
data.FireplaceQu.dtype
dtype('O')
data.FireplaceQu.fillna('No Fireplace' , inplace = True)
data.FireplaceQu.isnull().sum()
0
data.FireplaceQu.value_counts()
No Fireplace 988 Gd 530 TA 457 Fa 40 Ex 33 Po 25 Name: FireplaceQu, dtype: int64
data.FireplaceQu.value_counts().sum()
2073
sns.countplot(data.FireplaceQu)
plt.title('Count plot of FireplaceQu')
Text(0.5, 1.0, 'Count plot of FireplaceQu')
data.groupby("FireplaceQu")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| FireplaceQu | ||||||||
| Ex | 33.0 | 286554.666667 | 55664.331759 | 130500.0 | 239686.0 | 302000.0 | 340000.0 | 340000.0 |
| Fa | 40.0 | 167421.250000 | 32703.040099 | 117000.0 | 142875.0 | 158000.0 | 188500.0 | 262000.0 |
| Gd | 530.0 | 219530.394340 | 71625.390333 | 90350.0 | 160050.0 | 208200.0 | 274970.0 | 340000.0 |
| No Fireplace | 988.0 | 141606.623482 | 44270.114578 | 34900.0 | 112375.0 | 135625.0 | 165000.0 | 340000.0 |
| Po | 25.0 | 132467.320000 | 29351.985303 | 60000.0 | 122900.0 | 134000.0 | 154000.0 | 172000.0 |
| TA | 457.0 | 202245.312910 | 58543.393223 | 82500.0 | 163500.0 | 186500.0 | 236500.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ FireplaceQu', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) FireplaceQu 5.0 2.936194e+12 5.872387e+11 190.474012 3.623749e-167 Residual 2067.0 6.372641e+12 3.083039e+09 NaN NaN
t_FireplaceQu = pairwise_tukeyhsd(data.Property_Sale_Price, data.FireplaceQu)
print(t_FireplaceQu)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==============================================================================
group1 group2 meandiff p-adj lower upper reject
------------------------------------------------------------------------------
Ex Fa -119133.4167 0.001 -156378.3621 -81888.4712 True
Ex Gd -67024.2723 0.001 -95439.5674 -38608.9772 True
Ex No Fireplace -144948.0432 0.001 -172974.6362 -116921.4502 True
Ex Po -154087.3467 0.001 -196080.6366 -112094.0567 True
Ex TA -84309.3538 0.001 -112857.3643 -55761.3432 True
Fa Gd 52109.1443 0.001 26139.7169 78078.5718 True
Fa No Fireplace -25814.6265 0.0459 -51358.1596 -271.0934 True
Fa Po -34953.93 0.1335 -75332.3719 5424.5119 False
Fa TA 34824.0629 0.002 8709.487 60938.6388 True
Gd No Fireplace -77923.7709 0.001 -86451.0869 -69396.4549 True
Gd Po -87063.0743 0.001 -119476.985 -54649.1637 True
Gd TA -17285.0814 0.001 -27395.1807 -7174.9822 True
No Fireplace Po -9139.3035 0.9 -41213.008 22934.4011 False
No Fireplace TA 60638.6894 0.001 51679.0563 69598.3226 True
Po TA 69777.9929 0.001 37247.6761 102308.3097 True
------------------------------------------------------------------------------
data["FireplaceQu"].replace('Po', 'No Fireplace',inplace = True)
data.FireplaceQu.value_counts()
No Fireplace 1013 Gd 530 TA 457 Fa 40 Ex 33 Name: FireplaceQu, dtype: int64
data.FireplaceQu.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ FireplaceQu', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) FireplaceQu 4.0 2.934157e+12 7.335392e+11 237.966422 3.065384e-168 Residual 2068.0 6.374677e+12 3.082533e+09 NaN NaN
t_FireplaceQu = pairwise_tukeyhsd(data.Property_Sale_Price, data.FireplaceQu)
print(t_FireplaceQu)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=============================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------------------
Ex Fa -119133.4167 0.001 -154780.0274 -83486.8059 True
Ex Gd -67024.2723 0.001 -94220.1495 -39828.3951 True
Ex No Fireplace -145173.5936 0.001 -171986.7492 -118360.438 True
Ex TA -84309.3538 0.001 -111632.251 -56986.4565 True
Fa Gd 52109.1443 0.001 27254.1724 76964.1162 True
Fa No Fireplace -26040.1769 0.03 -50475.7904 -1604.5635 True
Fa TA 34824.0629 0.0014 9830.1716 59817.9543 True
Gd No Fireplace -78149.3213 0.001 -86275.4573 -70023.1853 True
Gd TA -17285.0814 0.001 -26961.3145 -7608.8484 True
No Fireplace TA 60864.2399 0.001 52322.6321 69405.8476 True
-----------------------------------------------------------------------------
sns.countplot(data.FireplaceQu)
plt.title('Count plot of FireplaceQu')
Text(0.5, 1.0, 'Count plot of FireplaceQu')
sns.catplot('FireplaceQu', 'Property_Sale_Price', data=data)
plt.title('Cat Plot og FireplaceQu vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot og FireplaceQu vs Property_Sale_Price')
sns.boxplot('FireplaceQu', 'Property_Sale_Price', data=data)
plt.title('Box Plot og FireplaceQu vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot og FireplaceQu vs Property_Sale_Price')
Good Pradictor
data.GarageType.isnull().sum()
113
data.GarageType.dtype
dtype('O')
data.GarageType.fillna('No Garage', inplace = True)
data.GarageType.isnull().sum()
0
data.GarageType.value_counts()
Attchd 1252 Detchd 537 BuiltIn 124 No Garage 113 Basment 27 CarPort 12 2Types 8 Name: GarageType, dtype: int64
data.GarageType.value_counts().sum()
2073
sns.countplot(data.GarageType)
plt.title('Count plot of GarageType')
Text(0.5, 1.0, 'Count plot of GarageType')
data.groupby("GarageType")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| GarageType | ||||||||
| 2Types | 8.0 | 149712.500000 | 29653.303237 | 87000.0 | 145000.0 | 147500.0 | 168750.0 | 186700.0 |
| Attchd | 1252.0 | 198469.570288 | 62778.142381 | 60000.0 | 149850.0 | 185000.0 | 237125.0 | 340000.0 |
| Basment | 27.0 | 156036.777778 | 55344.633028 | 55993.0 | 132250.0 | 145000.0 | 174000.0 | 340000.0 |
| BuiltIn | 124.0 | 241190.612903 | 68998.383109 | 91000.0 | 184587.5 | 227500.0 | 303477.0 | 340000.0 |
| CarPort | 12.0 | 109971.583333 | 21011.520630 | 82000.0 | 100100.0 | 109479.5 | 110000.0 | 164900.0 |
| Detchd | 537.0 | 132222.826816 | 38273.672570 | 35311.0 | 110000.0 | 129000.0 | 148000.0 | 340000.0 |
| No Garage | 113.0 | 106405.752212 | 32494.589352 | 34900.0 | 85000.0 | 106250.0 | 127500.0 | 200500.0 |
mod = ols('Property_Sale_Price ~ GarageType', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) GarageType 6.0 2.798865e+12 4.664775e+11 148.04104 1.756884e-156 Residual 2066.0 6.509969e+12 3.151001e+09 NaN NaN
t_GarageType = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageType)
print(t_GarageType)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ====================================================================== group1 group2 meandiff p-adj lower upper reject ---------------------------------------------------------------------- 2Types Attchd 48757.0703 0.1787 -10000.1608 107514.3013 False 2Types Basment 6324.2778 0.9 -60361.0597 73009.6153 False 2Types BuiltIn 91478.1129 0.001 31047.8705 151908.3553 True 2Types CarPort -39740.9167 0.6874 -115354.982 35873.1486 False 2Types Detchd -17489.6732 0.9 -76494.742 41515.3957 False 2Types No Garage -43306.7478 0.3481 -103914.9883 17301.4928 False Attchd Basment -42432.7925 0.002 -74656.4203 -10209.1647 True Attchd BuiltIn 42721.0426 0.001 27124.8203 58317.2649 True Attchd CarPort -88497.987 0.001 -136549.1558 -40446.8181 True Attchd Detchd -66246.7435 0.001 -74792.2748 -57701.2122 True Attchd No Garage -92063.8181 0.001 -108336.0882 -75791.5479 True Basment BuiltIn 85153.8351 0.001 49971.9622 120335.7081 True Basment CarPort -46065.1944 0.214 -103540.7271 11410.3383 False Basment Detchd -23813.951 0.323 -56487.3061 8859.4042 False Basment No Garage -49631.0256 0.001 -85117.7669 -14144.2842 True BuiltIn CarPort -131219.0296 0.001 -181302.1287 -81135.9304 True BuiltIn Detchd -108967.7861 0.001 -125473.1822 -92462.39 True BuiltIn No Garage -134784.8607 0.001 -156329.9012 -113239.8202 True CarPort Detchd 22251.2435 0.8008 -26102.6682 70605.1552 False CarPort No Garage -3565.8311 0.9 -53863.5592 46731.8969 False Detchd No Garage -25817.0746 0.001 -42962.7075 -8671.4417 True ----------------------------------------------------------------------
data["GarageType"].replace('2Types', 'Detchd',inplace = True)
data["GarageType"].replace('CarPort', 'No Garage',inplace = True)
data["GarageType"].replace('Basment', 'Detchd',inplace = True)
data.GarageType.value_counts()
Attchd 1252 Detchd 572 No Garage 125 BuiltIn 124 Name: GarageType, dtype: int64
data.GarageType.value_counts().sum()
2073
mod = ols('Property_Sale_Price ~ GarageType', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) GarageType 3.0 2.782040e+12 9.273466e+11 293.969762 6.068678e-159 Residual 2069.0 6.526794e+12 3.154565e+09 NaN NaN
t_GarageType = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageType)
print(t_GarageType)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ===================================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------------- Attchd BuiltIn 42721.0426 0.001 29125.7384 56316.3468 True Attchd Detchd -64878.0476 0.001 -72165.9904 -57590.1047 True Attchd No Garage -91721.4983 0.001 -105267.2315 -78175.7651 True BuiltIn Detchd -107599.0902 0.001 -121904.1106 -93294.0698 True BuiltIn No Garage -134442.5409 0.001 -152745.7206 -116139.3612 True Detchd No Garage -26843.4507 0.001 -41101.3679 -12585.5336 True ---------------------------------------------------------------------
sns.countplot(data.GarageType)
plt.title('Count plot of GarageType')
Text(0.5, 1.0, 'Count plot of GarageType')
sns.catplot('GarageType', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of GarageType vs Property_Sale_Price')
Text(0.5, 1.0, 'Cat Plot of GarageType vs Property_Sale_Price')
sns.boxplot('GarageType', 'Property_Sale_Price', data=data)
plt.title('Box Plot of GarageType vs Property_Sale_Price')
Text(0.5, 1.0, 'Box Plot of GarageType vs Property_Sale_Price')
Good Predictor
data.GarageYrBlt.isnull().sum()
113
data.GarageYrBlt.dtype
dtype('float64')
data.GarageYrBlt.sample(10)
1445 1990.0 1007 1970.0 291 NaN 1771 1995.0 2003 1960.0 370 2000.0 1736 2007.0 325 1941.0 175 1950.0 549 2003.0 Name: GarageYrBlt, dtype: float64
data.GarageYrBlt.median()
1984.0
data.GarageYrBlt.fillna(data.GarageYrBlt.median(), inplace = True)
data.GarageYrBlt.isnull().sum()
0
data.GarageYrBlt.describe()
count 2073.000000 mean 1980.613603 std 23.504410 min 1906.000000 25% 1964.000000 50% 1984.000000 75% 2003.000000 max 2019.000000 Name: GarageYrBlt, dtype: float64
data.GarageYrBlt.value_counts()
1984.0 122
2005.0 116
2006.0 82
2007.0 81
2003.0 72
...
2015.0 1
2019.0 1
1908.0 1
1906.0 1
2013.0 1
Name: GarageYrBlt, Length: 102, dtype: int64
data.GarageYrBlt.value_counts().sum()
2073
plt.hist(data.GarageYrBlt)
plt.title('Histogram of GarageYrBlt')
Text(0.5, 1.0, 'Histogram of GarageYrBlt')
sns.distplot(data.GarageYrBlt)
plt.title('Normality of GarageYrBlt')
Text(0.5, 1.0, 'Normality of GarageYrBlt')
sns.countplot(data.GarageYrBlt)
plt.title('Count plot of GarageYrBlt')
Text(0.5, 1.0, 'Count plot of GarageYrBlt')
sns.boxplot(data.GarageYrBlt, color = 'g')
plt.title('BoxPlot Of GarageYrBlt')
Text(0.5, 1.0, 'BoxPlot Of GarageYrBlt')
np.corrcoef(data.Property_Sale_Price, data.GarageYrBlt)
array([[1. , 0.47316836],
[0.47316836, 1. ]])
sns.scatterplot(data.Property_Sale_Price, data.GarageYrBlt)
plt.title('GarageYrBlt vs Property_Sale_Price')
Text(0.5, 1.0, 'GarageYrBlt vs Property_Sale_Price')
Not Good Predictor
data.GarageFinish.isnull().sum()
113
data.GarageFinish.dtype
dtype('O')
data.GarageFinish.fillna('No Garage', inplace = True)
data.GarageFinish.isnull().sum()
0
data.GarageFinish.value_counts()
Unf 857 RFn 605 Fin 498 No Garage 113 Name: GarageFinish, dtype: int64
data.GarageFinish.value_counts().sum()
2073
sns.countplot(data.GarageFinish)
plt.title('Count plot of GarageFinish')
Text(0.5, 1.0, 'Count plot of GarageFinish')
data.groupby("GarageFinish")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| GarageFinish | ||||||||
| Fin | 498.0 | 229552.206827 | 67955.485997 | 82500.0 | 178000.0 | 215000.0 | 285000.0 | 340000.0 |
| No Garage | 113.0 | 106405.752212 | 32494.589352 | 34900.0 | 85000.0 | 106250.0 | 127500.0 | 200500.0 |
| RFn | 605.0 | 200026.957025 | 54999.706561 | 68400.0 | 160000.0 | 190000.0 | 235000.0 | 340000.0 |
| Unf | 857.0 | 140947.771295 | 44510.885445 | 35311.0 | 115000.0 | 135000.0 | 155000.0 | 340000.0 |
mod = ols('Property_Sale_Price ~ GarageFinish', data=data).fit()
print(sm.stats.anova_lm(mod))
df sum_sq mean_sq F PR(>F) GarageFinish 3.0 3.372449e+12 1.124150e+12 391.79839 1.683598e-201 Residual 2069.0 5.936385e+12 2.869205e+09 NaN NaN
t_GarageFinish = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageFinish)
print(t_GarageFinish)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=======================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------------
Fin No Garage -123146.4546 0.001 -137497.0688 -108795.8405 True
Fin RFn -29525.2498 0.001 -37858.2074 -21192.2922 True
Fin Unf -88604.4355 0.001 -96364.5538 -80844.3173 True
No Garage RFn 93621.2048 0.001 79507.2381 107735.1715 True
No Garage Unf 34542.0191 0.001 20758.5032 48325.5349 True
RFn Unf -59079.1857 0.001 -66392.4179 -51765.9535 True
-----------------------------------------------------------------------
sns.catplot('GarageFinish', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of GarageType vs GarageFinish')
Text(0.5, 1.0, 'Cat Plot of GarageType vs GarageFinish')
sns.boxplot('GarageFinish', 'Property_Sale_Price', data=data)
plt.title('Box Plot of GarageType vs GarageFinish')
Text(0.5, 1.0, 'Box Plot of GarageType vs GarageFinish')
Good Predictor
data.GarageCars.isnull().sum()
0
data.GarageCars.describe()
count 2073.000000 mean 1.775687 std 0.741976 min 0.000000 25% 1.000000 50% 2.000000 75% 2.000000 max 4.000000 Name: GarageCars, dtype: float64
data.GarageCars.value_counts()
2 1185 1 510 3 259 0 113 4 6 Name: GarageCars, dtype: int64
data.GarageCars.value_counts().sum()
2073
sns.countplot(data.GarageCars)
plt.title('GarageCars - count')
Text(0.5, 1.0, 'GarageCars - count')
data.groupby("GarageCars")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| GarageCars | ||||||||
| 0 | 113.0 | 106405.752212 | 32494.589352 | 34900.0 | 85000.0 | 106250.0 | 127500.00 | 200500.0 |
| 1 | 510.0 | 128228.098039 | 33074.896644 | 35311.0 | 110000.0 | 128000.0 | 141000.00 | 266500.0 |
| 2 | 1185.0 | 182900.022785 | 50169.588399 | 55993.0 | 147400.0 | 177000.0 | 211000.00 | 340000.0 |
| 3 | 259.0 | 280939.895753 | 62830.981922 | 81000.0 | 245000.0 | 293077.0 | 340000.00 | 340000.0 |
| 4 | 6.0 | 204876.333333 | 55778.983715 | 123000.0 | 176000.0 | 203150.0 | 251059.25 | 265979.0 |
mod_GC = ols('Property_Sale_Price ~ GarageCars', data = data).fit()
sm.stats.anova_lm(mod_GC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| GarageCars | 1.0 | 4.096521e+12 | 4.096521e+12 | 1627.664126 | 4.128337e-263 |
| Residual | 2071.0 | 5.212313e+12 | 2.516810e+09 | NaN | NaN |
tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageCars, alpha = 0.05)
print(tukey_GC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------
0 1 21822.3458 0.001 8305.1442 35339.5475 True
0 2 76494.2706 0.001 63694.3867 89294.1544 True
0 3 174534.1435 0.001 159876.9965 189191.2906 True
0 4 98470.5811 0.001 44004.5521 152936.6102 True
1 2 54671.9247 0.001 47786.8657 61556.9838 True
1 3 152711.7977 0.001 142792.1721 162631.4233 True
1 4 76648.2353 0.001 23261.7639 130034.7067 True
2 3 98039.873 0.001 89122.4037 106957.3422 True
2 4 21976.3105 0.7654 -31233.0647 75185.6858 False
3 4 -76063.5624 0.0011 -129749.9904 -22377.1344 True
----------------------------------------------------------------
data["GarageCars"].replace(4,2,inplace = True)
data.GarageCars.value_counts()
2 1191 1 510 3 259 0 113 Name: GarageCars, dtype: int64
data.GarageCars.value_counts().sum()
2073
mod_GC = ols('Property_Sale_Price ~ GarageCars', data = data).fit()
sm.stats.anova_lm(mod_GC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| GarageCars | 1.0 | 4.164913e+12 | 4.164913e+12 | 1676.840447 | 4.702769e-269 |
| Residual | 2071.0 | 5.143921e+12 | 2.483786e+09 | NaN | NaN |
tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageCars, alpha = 0.05)
print(tukey_GC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==============================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------
0 1 21822.3458 0.001 9091.7614 34552.9302 True
0 2 76604.9825 0.001 64552.6162 88657.3487 True
0 3 174534.1435 0.001 160729.9515 188338.3356 True
1 2 54782.6366 0.001 48303.1617 61262.1116 True
1 3 152711.7977 0.001 143369.4328 162054.1626 True
2 3 97929.1611 0.001 89534.4285 106323.8936 True
--------------------------------------------------------------
sns.countplot(x = 'GarageCars', data = data)
plt.title('GarageCars - count')
Text(0.5, 1.0, 'GarageCars - count')
sns.catplot(x = 'GarageCars', y = 'Property_Sale_Price',data = data)
plt.title('Catplot Of GarageCars vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of GarageCars vs Property_Sale_Price')
sns.boxplot(x = 'GarageCars', y = 'Property_Sale_Price',data = data)
plt.title('Boxplot Of GarageCars vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot Of GarageCars vs Property_Sale_Price')
GOOD PREDICTOR
data.GarageArea.isnull().sum()
0
data.GarageArea.describe()
count 2073.000000 mean 474.683068 std 212.284955 min 0.000000 25% 336.000000 50% 480.000000 75% 576.000000 max 1418.000000 Name: GarageArea, dtype: float64
data.GarageArea.value_counts()
0 113
440 72
576 61
240 54
528 53
...
350 1
342 1
283 1
292 1
770 1
Name: GarageArea, Length: 441, dtype: int64
data.GarageArea.value_counts().sum()
2073
sns.displot(data.GarageArea)
plt.title('Histogram Of GarageArea')
Text(0.5, 1.0, 'Histogram Of GarageArea')
sns.distplot(data.GarageArea)
plt.title('Normality Of GarageArea')
Text(0.5, 1.0, 'Normality Of GarageArea')
sns.countplot(data.GarageArea)
plt.title('GarageArea - count')
Text(0.5, 1.0, 'GarageArea - count')
sns.boxplot(data.GarageArea, color = 'red')
plt.title('BoxPlot Of GarageArea')
Text(0.5, 1.0, 'BoxPlot Of GarageArea')
IQR = data.GarageArea.quantile(0.75) - data.GarageArea.quantile(0.25)
IQR
240.0
UL = data.GarageArea.quantile(0.75) + (1.5*IQR)
UL
936.0
LL = data.GarageArea.quantile(0.25) - (1.5*IQR)
LL
-24.0
data.GarageArea[(data.GarageArea > UL)].value_counts().sum()
27
data.GarageArea[(data.GarageArea < LL)].value_counts().sum()
0
data['GarageArea'] = np.where(data['GarageArea'] > UL, UL, data['GarageArea'])
sns.boxplot(data.GarageArea, color = 'green')
plt.title('BoxPlot Of GarageArea')
Text(0.5, 1.0, 'BoxPlot Of GarageArea')
sns.distplot(data.GarageArea)
plt.title('Normality Of GarageArea')
Text(0.5, 1.0, 'Normality Of GarageArea')
np.corrcoef(data.GarageArea, data.Property_Sale_Price)
array([[1. , 0.65435751],
[0.65435751, 1. ]])
sns.scatterplot(x = data['GarageArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('GarageArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of GarageArea vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter Plot Of GarageArea vs Property_Sale_Price')
GOOD PREDICTOR
data.GarageQual.isnull().sum()
113
data.GarageQual.dtype
dtype('O')
data['GarageQual'] = data['GarageQual'].fillna('No Garage')
data.GarageQual.isnull().sum()
0
data.GarageQual.value_counts()
TA 1870 No Garage 113 Fa 65 Gd 18 Ex 4 Po 3 Name: GarageQual, dtype: int64
data.GarageQual.value_counts().sum()
2073
sns.countplot(data.GarageQual)
plt.title('GarageQual - count')
Text(0.5, 1.0, 'GarageQual - count')
data.groupby("GarageQual")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| GarageQual | ||||||||
| Ex | 4.0 | 178875.000000 | 107467.340000 | 120500.0 | 125750.0 | 127500.0 | 180625.0 | 340000.0 |
| Fa | 65.0 | 121509.553846 | 46472.205090 | 64500.0 | 91000.0 | 112000.0 | 135000.0 | 256000.0 |
| Gd | 18.0 | 225778.888889 | 73079.130029 | 90350.0 | 165975.0 | 236115.0 | 295242.5 | 325000.0 |
| No Garage | 113.0 | 106405.752212 | 32494.589352 | 34900.0 | 85000.0 | 106250.0 | 127500.0 | 200500.0 |
| Po | 3.0 | 100166.666667 | 35143.752408 | 67000.0 | 81750.0 | 96500.0 | 116750.0 | 137000.0 |
| TA | 1870.0 | 183501.287166 | 65515.593003 | 35311.0 | 135500.0 | 171000.0 | 219875.0 | 340000.0 |
mod_GQ = ols('Property_Sale_Price ~ GarageQual', data = data).fit()
sm.stats.anova_lm(mod_GQ)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| GarageQual | 5.0 | 9.021521e+11 | 1.804304e+11 | 44.363481 | 1.349928e-43 |
| Residual | 2067.0 | 8.406682e+12 | 4.067093e+09 | NaN | NaN |
tukey_GQ = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageQual, alpha = 0.05)
print(tukey_GQ)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=======================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------------
Ex Fa -57365.4462 0.5006 -151074.8873 36343.995 False
Ex Gd 46903.8889 0.7405 -53648.0777 147455.8555 False
Ex No Garage -72469.2478 0.2228 -165017.7064 20079.2108 False
Ex Po -78708.3333 0.5758 -217640.8407 60224.174 False
Ex TA 4626.2872 0.9 -86423.6124 95676.1867 False
Fa Gd 104269.335 0.001 55819.5793 152719.0908 True
Fa No Garage -15103.8016 0.6304 -43421.606 13214.0028 False
Fa Po -21342.8872 0.9 -128762.2637 86076.4893 False
Fa TA 61991.7333 0.001 39040.3704 84943.0962 True
Gd No Garage -119373.1367 0.001 -165537.3724 -73208.9009 True
Gd Po -125612.2222 0.02 -239050.1394 -12174.305 True
Gd TA -42277.6017 0.058 -85358.9628 803.7594 False
No Garage Po -6239.0855 0.9 -112647.1693 100168.9982 False
No Garage TA 77095.535 0.001 59473.8753 94717.1946 True
Po TA 83334.6205 0.2105 -21772.6927 188441.9337 False
-----------------------------------------------------------------------
data["GarageQual"].replace('Ex','TA',inplace = True)
data["GarageQual"].replace('Po','Fa',inplace = True)
data.GarageQual.value_counts()
TA 1874 No Garage 113 Fa 68 Gd 18 Name: GarageQual, dtype: int64
data.GarageQual.value_counts().sum()
2073
mod_GQ = ols('Property_Sale_Price ~ GarageQual', data = data).fit()
sm.stats.anova_lm(mod_GQ)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| GarageQual | 3.0 | 9.007604e+11 | 3.002535e+11 | 73.884274 | 2.143585e-45 |
| Residual | 2069.0 | 8.408074e+12 | 4.063835e+09 | NaN | NaN |
tukey_GQ = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageQual, alpha = 0.05)
print(tukey_GQ)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
======================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------------
Fa Gd 105210.933 0.001 61764.9599 148656.9062 True
Fa No Garage -14162.2037 0.4708 -39317.9161 10993.5088 False
Fa TA 62923.4566 0.001 42689.7068 83157.2064 True
Gd No Garage -119373.1367 0.001 -160969.1 -77777.1734 True
Gd TA -42287.4764 0.0264 -81105.242 -3469.7108 True
No Garage TA 77085.6603 0.001 61208.7522 92962.5684 True
----------------------------------------------------------------------
data["GarageQual"].replace('Fa', 'No Garage',inplace = True)
data.GarageQual.value_counts()
TA 1874 No Garage 181 Gd 18 Name: GarageQual, dtype: int64
data.GarageQual.value_counts().sum()
2073
mod_GQ = ols('Property_Sale_Price ~ GarageQual', data = data).fit()
sm.stats.anova_lm(mod_GQ)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| GarageQual | 2.0 | 8.922457e+11 | 4.461228e+11 | 109.720736 | 5.119506e-46 |
| Residual | 2070.0 | 8.416588e+12 | 4.065985e+09 | NaN | NaN |
tukey_GQ = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageQual, alpha = 0.05)
print(tukey_GQ)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=======================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------------
Gd No Garage -114052.5298 0.001 -151014.3784 -77090.6812 True
Gd TA -42287.4764 0.0143 -77706.9584 -6867.9944 True
No Garage TA 71765.0534 0.001 60124.2081 83405.8987 True
-----------------------------------------------------------------------
sns.countplot(x = 'GarageQual', data = data)
plt.title('GarageQual - count')
Text(0.5, 1.0, 'GarageQual - count')
sns.catplot(x = 'GarageQual', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of GarageQual vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of GarageQual vs Property_Sale_Price')
sns.boxplot(x = 'GarageQual', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of GarageQual vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot Of GarageQual vs Property_Sale_Price')
Good Predictor
data.GarageCond.isnull().sum()
113
data.GarageCond.dtype
dtype('O')
data['GarageCond'] = data['GarageCond'].fillna('No Garage')
data.GarageCond.value_counts().sum()
2073
data.GarageCond.value_counts()
TA 1885 No Garage 113 Fa 47 Gd 17 Po 8 Ex 3 Name: GarageCond, dtype: int64
data.GarageCond.value_counts().sum()
2073
sns.countplot(data.GarageCond)
plt.title('GarageCond - count')
Text(0.5, 1.0, 'GarageCond - count')
data.groupby("GarageCond")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| GarageCond | ||||||||
| Ex | 3.0 | 125166.666667 | 4041.451884 | 120500.0 | 124000.0 | 127500.0 | 127500.0 | 127500.0 |
| Fa | 47.0 | 109221.085106 | 39440.028632 | 40000.0 | 80000.0 | 100000.0 | 137250.0 | 220000.0 |
| Gd | 17.0 | 187108.235294 | 71354.376463 | 118500.0 | 144000.0 | 145000.0 | 274970.0 | 302000.0 |
| No Garage | 113.0 | 106405.752212 | 32494.589352 | 34900.0 | 85000.0 | 106250.0 | 127500.0 | 200500.0 |
| Po | 8.0 | 110062.500000 | 21441.677540 | 67000.0 | 103625.0 | 114500.0 | 121750.0 | 137000.0 |
| TA | 1885.0 | 183848.974536 | 65658.153844 | 35311.0 | 135000.0 | 171500.0 | 221000.0 | 340000.0 |
mod_GC = ols('Property_Sale_Price ~ GarageCond', data = data).fit()
sm.stats.anova_lm(mod_GC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| GarageCond | 5.0 | 9.123947e+11 | 1.824789e+11 | 44.921892 | 3.893997e-44 |
| Residual | 2067.0 | 8.396440e+12 | 4.062138e+09 | NaN | NaN |
tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageCond, alpha = 0.05)
print(tukey_GC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
======================================================================
group1 group2 meandiff p-adj lower upper reject
----------------------------------------------------------------------
Ex Fa -15945.5816 0.9 -124202.6326 92311.4695 False
Ex Gd 61941.5686 0.6127 -51902.565 175785.7023 False
Ex No Garage -18760.9145 0.9 -125104.1555 87582.3266 False
Ex Po -15104.1667 0.9 -138179.6269 107971.2936 False
Ex TA 58682.3079 0.5887 -46360.2859 163724.9016 False
Fa Gd 77887.1502 0.001 26435.7327 129338.5677 True
Fa No Garage -2815.3329 0.9 -34369.1997 28738.5339 False
Fa Po 841.4149 0.9 -68687.935 70370.7648 False
Fa TA 74627.8894 0.001 47781.8823 101473.8966 True
Gd No Garage -80702.4831 0.001 -127994.5985 -33410.3677 True
Gd Po -77045.7353 0.0548 -154989.4853 898.0148 False
Gd TA -3259.2608 0.9 -47549.2796 41030.7581 False
No Garage Po 3656.7478 0.9 -62853.5997 70167.0952 False
No Garage TA 77443.2223 0.001 59836.2943 95050.1504 True
Po TA 73786.4745 0.014 9376.1655 138196.7836 True
----------------------------------------------------------------------
data["GarageCond"].replace('Fa','No Garage',inplace = True)
data["GarageCond"].replace('Ex','No Garage',inplace = True)
data["GarageCond"].replace('Po','No Garage',inplace = True)
data["GarageCond"].replace('Gd','TA',inplace = True)
data.GarageCond.value_counts()
TA 1902 No Garage 171 Name: GarageCond, dtype: int64
data.GarageCond.value_counts().sum()
2073
GC_TA = data[data.GarageCond == 'TA']
GC_NG = data[data.GarageCond == 'No Garage']
ttest_ind(GC_TA.Property_Sale_Price, GC_NG.Property_Sale_Price)
Ttest_indResult(statistic=14.988371426457498, pvalue=2.7101983787665408e-48)
sns.countplot(x = 'GarageCond', data = data)
plt.title('GarageCond - count')
Text(0.5, 1.0, 'GarageCond - count')
sns.catplot(x = 'GarageCond', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of GarageCond vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of GarageCond vs Property_Sale_Price')
sns.boxplot(x = 'GarageCond', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of GarageCond vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot Of GarageCond vs Property_Sale_Price')
Good Predictor
data.PavedDrive.isnull().sum()
0
data.PavedDrive.value_counts()
Y 1913 N 116 P 44 Name: PavedDrive, dtype: int64
data.PavedDrive.value_counts().sum()
2073
sns.countplot(data.PavedDrive)
plt.title('PavedDrive - count')
Text(0.5, 1.0, 'PavedDrive - count')
data.groupby("PavedDrive")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| PavedDrive | ||||||||
| N | 116.0 | 120372.413793 | 45249.930170 | 34900.0 | 86750.0 | 113752.0 | 145225.0 | 265979.0 |
| P | 44.0 | 135432.954545 | 31840.985624 | 79500.0 | 104250.0 | 142000.0 | 160000.0 | 215000.0 |
| Y | 1913.0 | 182031.964454 | 66808.834987 | 35311.0 | 133900.0 | 169990.0 | 220000.0 | 340000.0 |
mod_PD = ols('Property_Sale_Price ~ PavedDrive', data = data).fit()
sm.stats.anova_lm(mod_PD)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| PavedDrive | 2.0 | 4.957099e+11 | 2.478549e+11 | 58.215417 | 2.528211e-25 |
| Residual | 2070.0 | 8.813124e+12 | 4.257548e+09 | NaN | NaN |
tukey_PD = pairwise_tukeyhsd(data.Property_Sale_Price, data.PavedDrive, alpha = 0.05)
print(tukey_PD)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=============================================================
group1 group2 meandiff p-adj lower upper reject
-------------------------------------------------------------
N P 15060.5408 0.3948 -12035.4007 42156.4822 False
N Y 61659.5507 0.001 47025.8512 76293.2501 True
P Y 46599.0099 0.001 23263.8288 69934.191 True
-------------------------------------------------------------
data["PavedDrive"].replace('P', 'N',inplace = True)
data.PavedDrive.value_counts()
Y 1913 N 160 Name: PavedDrive, dtype: int64
data.PavedDrive.value_counts().sum()
2073
PD_Y = data[data.PavedDrive == 'Y']
PD_N = data[data.PavedDrive == "N"]
ttest_ind(PD_Y.Property_Sale_Price, PD_N.Property_Sale_Price)
Ttest_indResult(statistic=10.709464579232613, pvalue=4.366284091297098e-26)
sns.countplot(x = 'PavedDrive', data = data)
plt.title('PavedDrive - count')
Text(0.5, 1.0, 'PavedDrive - count')
sns.catplot(x = 'PavedDrive', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of PavedDrive vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of PavedDrive vs Property_Sale_Price')
sns.boxplot(x = 'PavedDrive', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of PavedDrive vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot Of PavedDrive vs Property_Sale_Price')
Good Predictor
data.WoodDeckSF.isnull().sum()
0
data.WoodDeckSF.describe()
count 2073.000000 mean 94.168837 std 129.072113 min 0.000000 25% 0.000000 50% 0.000000 75% 168.000000 max 857.000000 Name: WoodDeckSF, dtype: float64
data.WoodDeckSF.value_counts()
0 1089
192 51
100 51
144 45
168 43
...
35 1
45 1
126 1
362 1
476 1
Name: WoodDeckSF, Length: 274, dtype: int64
data.WoodDeckSF.value_counts().sum()
2073
sns.displot(data.WoodDeckSF)
plt.title('Histogram Of WoodDeckSF')
Text(0.5, 1.0, 'Histogram Of WoodDeckSF')
sns.distplot(data.WoodDeckSF)
plt.title('Normality Of WoodDeckSF')
Text(0.5, 1.0, 'Normality Of WoodDeckSF')
sns.countplot(data.WoodDeckSF)
plt.title('WoodDeckSF - count')
Text(0.5, 1.0, 'WoodDeckSF - count')
sns.boxplot(data.WoodDeckSF, color = 'red')
plt.title('BoxPlot Of WoodDeckSF')
Text(0.5, 1.0, 'BoxPlot Of WoodDeckSF')
IQR = data.WoodDeckSF.quantile(0.75) - data.WoodDeckSF.quantile(0.25)
IQR
168.0
UL = data.WoodDeckSF.quantile(0.75) + (1.5*IQR)
UL
420.0
LL = data.WoodDeckSF.quantile(0.25) - (1.5*IQR)
LL
-252.0
data.WoodDeckSF[(data.WoodDeckSF > UL)].value_counts().sum()
50
data.WoodDeckSF[(data.WoodDeckSF < LL)].value_counts().sum()
0
data['WoodDeckSF'] = np.where(data['WoodDeckSF'] > UL, UL, data['WoodDeckSF'])
sns.boxplot(data.WoodDeckSF, color = 'green')
plt.title('BoxPlot Of WoodDeckSF')
Text(0.5, 1.0, 'BoxPlot Of WoodDeckSF')
sns.distplot(data.WoodDeckSF)
plt.title('Normality Of WoodDeckSF')
Text(0.5, 1.0, 'Normality Of WoodDeckSF')
np.corrcoef(data.WoodDeckSF, data.Property_Sale_Price)
array([[1. , 0.34254742],
[0.34254742, 1. ]])
Not Good Predictor
data.OpenPorchSF.isnull().sum()
0
data.OpenPorchSF.describe()
count 2073.000000 mean 46.035697 std 64.768713 min 0.000000 25% 0.000000 50% 24.000000 75% 67.000000 max 547.000000 Name: OpenPorchSF, dtype: float64
data.OpenPorchSF.value_counts()
0 939
36 42
20 31
48 30
40 29
...
119 1
123 1
15 1
129 1
547 1
Name: OpenPorchSF, Length: 202, dtype: int64
data.OpenPorchSF.value_counts().sum()
2073
sns.displot(data.OpenPorchSF)
plt.title('Histogram Of OpenPorchSF')
Text(0.5, 1.0, 'Histogram Of OpenPorchSF')
sns.distplot(data.OpenPorchSF)
plt.title('Normality Of OpenPorchSF')
Text(0.5, 1.0, 'Normality Of OpenPorchSF')
sns.countplot(data.OpenPorchSF)
plt.title('OpenPorchSF - count')
Text(0.5, 1.0, 'OpenPorchSF - count')
sns.boxplot(data.OpenPorchSF, color = 'red')
plt.title('BoxPlot Of OpenPorchSF')
Text(0.5, 1.0, 'BoxPlot Of OpenPorchSF')
IQR = data.OpenPorchSF.quantile(0.75) - data.OpenPorchSF.quantile(0.25)
IQR
67.0
UL = data.OpenPorchSF.quantile(0.75) + (1.5*IQR)
UL
167.5
LL = data.OpenPorchSF.quantile(0.25) - (1.5*IQR)
LL
-100.5
data.OpenPorchSF[(data.OpenPorchSF > UL)].value_counts().sum()
118
data.OpenPorchSF[(data.OpenPorchSF < LL)].value_counts().sum()
0
data['OpenPorchSF'] = np.where(data['OpenPorchSF'] > UL, UL, data['OpenPorchSF'])
sns.boxplot(data.OpenPorchSF, color = 'green')
plt.title('BoxPlot Of OpenPorchSF')
Text(0.5, 1.0, 'BoxPlot Of OpenPorchSF')
sns.distplot(data.OpenPorchSF)
plt.title('Normality Of OpenPorchSF')
Text(0.5, 1.0, 'Normality Of OpenPorchSF')
np.corrcoef(data.OpenPorchSF, data.Property_Sale_Price)
array([[1. , 0.42627987],
[0.42627987, 1. ]])
Not Good Predictor
data.EnclosedPorch.isnull().sum()
0
data.EnclosedPorch.describe()
count 2073.000000 mean 21.029908 std 59.569928 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 552.000000 Name: EnclosedPorch, dtype: float64
data.EnclosedPorch.value_counts()
0 1790
112 23
216 8
192 7
154 7
...
248 1
254 1
268 1
272 1
301 1
Name: EnclosedPorch, Length: 120, dtype: int64
data.EnclosedPorch.value_counts().sum()
2073
sns.displot(data.EnclosedPorch)
plt.title('Histogram Of EnclosedPorch')
Text(0.5, 1.0, 'Histogram Of EnclosedPorch')
sns.distplot(data.EnclosedPorch)
plt.title('Normality Of EnclosedPorch')
Text(0.5, 1.0, 'Normality Of EnclosedPorch')
sns.countplot(data.EnclosedPorch)
plt.title('EnclosedPorch - count')
Text(0.5, 1.0, 'EnclosedPorch - count')
sns.boxplot(data.EnclosedPorch, color = 'red')
plt.title('BoxPlot Of EnclosedPorch')
Text(0.5, 1.0, 'BoxPlot Of EnclosedPorch')
IQR = data.EnclosedPorch.quantile(0.75) - data.EnclosedPorch.quantile(0.25)
IQR
0.0
UL = data.EnclosedPorch.quantile(0.75) + (1.5*IQR)
UL
0.0
LL = data.EnclosedPorch.quantile(0.25) - (1.5*IQR)
LL
0.0
np.corrcoef(data.EnclosedPorch, data.Property_Sale_Price)
array([[ 1. , -0.11479018],
[-0.11479018, 1. ]])
mod_EP = ols('Property_Sale_Price ~ EnclosedPorch', data = data).fit()
sm.stats.anova_lm(mod_EP)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| EnclosedPorch | 1.0 | 1.226605e+11 | 1.226605e+11 | 27.653506 | 1.600557e-07 |
| Residual | 2071.0 | 9.186174e+12 | 4.435622e+09 | NaN | NaN |
tukey_EP = pairwise_tukeyhsd(data.Property_Sale_Price, data.EnclosedPorch, alpha = 0.05)
print(tukey_EP)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
0 19 37657.3642 0.9 -165235.9802 240550.7087 False
0 20 -60342.6358 0.9 -347197.0841 226511.8126 False
0 24 -100342.6358 0.9 -387197.0841 186511.8126 False
0 30 -78342.6358 0.9 -365197.0841 208511.8126 False
0 32 -89167.6358 0.9 -232714.933 54379.6615 False
0 34 -63667.6358 0.9 -266560.9802 139225.7087 False
0 36 -32892.6358 0.9 -235785.9802 170000.7087 False
0 37 157657.3642 0.9 -129197.0841 444511.8126 False
0 39 -1592.6358 0.9 -204485.9802 201300.7087 False
0 40 -21225.9691 0.9 -186933.9072 144481.9691 False
0 42 -13342.6358 0.9 -300197.0841 273511.8126 False
0 44 -18842.6358 0.9 -221735.9802 184050.7087 False
0 48 -91342.6358 0.9 -294235.9802 111550.7087 False
0 50 -22342.6358 0.9 -225235.9802 180550.7087 False
0 52 -3142.6358 0.9 -289997.0841 283711.8126 False
0 54 -49342.6358 0.9 -336197.0841 237511.8126 False
0 56 -22908.6358 0.9 -309763.0841 263945.8126 False
0 60 -75342.6358 0.9 -362197.0841 211511.8126 False
0 64 -42342.6358 0.9 -329197.0841 244511.8126 False
0 67 -44842.6358 0.9 -331697.0841 242011.8126 False
0 68 -24842.6358 0.9 -311697.0841 262011.8126 False
0 70 -2342.6358 0.9 -205235.9802 200550.7087 False
0 77 -100675.9691 0.9 -266383.9072 65031.9691 False
0 80 -72192.6358 0.9 -275085.9802 130700.7087 False
0 81 -43342.6358 0.9 -330197.0841 243511.8126 False
0 84 -55842.6358 0.9 -258735.9802 147050.7087 False
0 87 -113842.6358 0.9 -400697.0841 173011.8126 False
0 90 -95342.6358 0.9 -382197.0841 191511.8126 False
0 91 -106342.6358 0.9 -393197.0841 180511.8126 False
0 94 -96342.6358 0.9 -299235.9802 106550.7087 False
0 96 -80675.9691 0.9 -197947.1604 36595.2222 False
0 98 -79566.6358 0.9 -366421.0841 207287.8126 False
0 99 557.3642 0.9 -127871.0205 128985.749 False
0 100 -121342.6358 0.9 -324235.9802 81550.7087 False
0 102 -61442.6358 0.9 -178713.8271 55828.5556 False
0 105 -63342.6358 0.9 -350197.0841 223511.8126 False
0 108 -45675.9691 0.9 -211383.9072 120031.9691 False
0 112 -60875.0271 0.0413 -121054.5555 -695.4986 True
0 114 -39342.6358 0.9 -167771.0205 89085.749 False
0 115 -101392.6358 0.9 -304285.9802 101500.7087 False
0 116 -57717.6358 0.9 -201264.933 85829.6615 False
0 120 -52442.6358 0.9 -180871.0205 75985.749 False
0 123 -27342.6358 0.9 -314197.0841 259511.8126 False
0 126 -39842.6358 0.9 -205550.5739 125865.3024 False
0 128 -73467.6358 0.9 -217014.933 70079.6615 False
0 129 -93342.6358 0.9 -380197.0841 193511.8126 False
0 130 -67342.6358 0.9 -354197.0841 219511.8126 False
0 134 -54342.6358 0.9 -257235.9802 148550.7087 False
0 136 -54342.6358 0.9 -341197.0841 232511.8126 False
0 137 -58342.6358 0.9 -261235.9802 144550.7087 False
0 138 -80342.6358 0.9 -283235.9802 122550.7087 False
0 140 -82342.6358 0.9 -369197.0841 204511.8126 False
0 143 -22842.6358 0.9 -225735.9802 180050.7087 False
0 144 -45914.0643 0.9 -154516.3122 62688.1836 False
0 145 7657.3642 0.9 -279197.0841 294511.8126 False
0 148 -74842.6358 0.9 -361697.0841 212011.8126 False
0 150 -18642.6358 0.9 -147071.0205 109785.749 False
0 154 -22942.6358 0.9 -131544.8837 85659.6122 False
0 156 -37342.6358 0.9 -145944.8837 71259.6122 False
0 158 -37875.9691 0.9 -203583.9072 127831.9691 False
0 160 -53342.6358 0.9 -219050.5739 112365.3024 False
0 162 152657.3642 0.1661 -13050.5739 318365.3024 False
0 164 -74862.6358 0.9 -203291.0205 53565.749 False
0 168 -54175.9691 0.9 -219883.9072 111531.9691 False
0 169 -7342.6358 0.9 -294197.0841 279511.8126 False
0 170 31157.3642 0.9 -171735.9802 234050.7087 False
0 172 -142342.6358 0.9 -429197.0841 144511.8126 False
0 174 104657.3642 0.9 -98235.9802 307550.7087 False
0 176 -59709.3024 0.9 -225417.2406 105998.6357 False
0 177 16157.3642 0.9 -270697.0841 303011.8126 False
0 180 -23592.6358 0.9 -226485.9802 179300.7087 False
0 183 -30342.6358 0.9 -317197.0841 256511.8126 False
0 184 -55009.3024 0.9 -220717.2406 110698.6357 False
0 185 -33342.6358 0.9 -176889.933 110204.6615 False
0 189 -29342.6358 0.9 -316197.0841 257511.8126 False
0 190 -73092.6358 0.9 -275985.9802 129800.7087 False
0 192 -4842.6358 0.9 -113444.8837 103759.6122 False
0 194 -28442.6358 0.9 -315297.0841 258411.8126 False
0 196 -43342.6358 0.9 -246235.9802 159550.7087 False
0 198 -77342.6358 0.9 -364197.0841 209511.8126 False
0 200 18281.3642 0.9 -268573.0841 305135.8126 False
0 202 41357.3642 0.9 -161535.9802 244250.7087 False
0 205 -46109.3024 0.9 -211817.2406 119598.6357 False
0 208 -65342.6358 0.9 -352197.0841 221511.8126 False
0 210 -76092.6358 0.9 -362947.0841 210761.8126 False
0 212 -19342.6358 0.9 -222235.9802 183550.7087 False
0 214 -58342.6358 0.9 -345197.0841 228511.8126 False
0 216 45032.3642 0.9 -56583.9986 146648.7271 False
0 218 -35342.6358 0.9 -322197.0841 251511.8126 False
0 220 -49342.6358 0.9 -336197.0841 237511.8126 False
0 221 -25342.6358 0.9 -312197.0841 261511.8126 False
0 224 52407.3642 0.9 -91139.933 195954.6615 False
0 226 -97342.6358 0.9 -300235.9802 105550.7087 False
0 228 -21092.6358 0.9 -164639.933 122454.6615 False
0 230 -28842.6358 0.9 -315697.0841 258011.8126 False
0 234 60324.0309 0.9 -105383.9072 226031.9691 False
0 236 -98701.1358 0.9 -301594.4802 104192.2087 False
0 239 -47342.6358 0.9 -334197.0841 239511.8126 False
0 240 -45092.6358 0.9 -247985.9802 157800.7087 False
0 242 -55342.6358 0.9 -342197.0841 231511.8126 False
0 244 -66917.6358 0.9 -210464.933 76629.6615 False
0 248 -72342.6358 0.9 -359197.0841 214511.8126 False
0 252 -29222.6358 0.9 -157651.0205 99205.749 False
0 254 -42342.6358 0.9 -329197.0841 244511.8126 False
0 259 -52842.6358 0.9 -339697.0841 234011.8126 False
0 264 -25009.3024 0.9 -190717.2406 140698.6357 False
0 268 32157.3642 0.9 -254697.0841 319011.8126 False
0 272 -42342.6358 0.9 -329197.0841 244511.8126 False
0 275 -24442.6358 0.9 -311297.0841 262411.8126 False
0 280 47657.3642 0.9 -239197.0841 334511.8126 False
0 286 -103342.6358 0.9 -390197.0841 183511.8126 False
0 291 157657.3642 0.9 -129197.0841 444511.8126 False
0 293 -58342.6358 0.9 -345197.0841 228511.8126 False
0 294 -52342.6358 0.9 -339197.0841 234511.8126 False
0 301 6657.3642 0.9 -280197.0841 293511.8126 False
0 318 62057.3642 0.9 -103650.5739 227765.3024 False
0 330 -38342.6358 0.9 -325197.0841 248511.8126 False
0 386 83636.3642 0.9 -119256.9802 286529.7087 False
0 552 52657.3642 0.9 -234197.0841 339511.8126 False
19 20 -98000.0 0.9 -449225.4205 253225.4205 False
19 24 -138000.0 0.9 -489225.4205 213225.4205 False
19 30 -116000.0 0.9 -467225.4205 235225.4205 False
19 32 -126825.0 0.9 -375178.8766 121528.8766 False
19 34 -101325.0 0.9 -388099.355 185449.355 False
19 36 -70550.0 0.9 -357324.355 216224.355 False
19 37 120000.0 0.9 -231225.4205 471225.4205 False
19 39 -39250.0 0.9 -326024.355 247524.355 False
19 40 -58883.3333 0.9 -320671.3052 202904.6386 False
19 42 -51000.0 0.9 -402225.4205 300225.4205 False
19 44 -56500.0 0.9 -343274.355 230274.355 False
19 48 -129000.0 0.9 -415774.355 157774.355 False
19 50 -60000.0 0.9 -346774.355 226774.355 False
19 52 -40800.0 0.9 -392025.4205 310425.4205 False
19 54 -87000.0 0.9 -438225.4205 264225.4205 False
19 56 -60566.0 0.9 -411791.4205 290659.4205 False
19 60 -113000.0 0.9 -464225.4205 238225.4205 False
19 64 -80000.0 0.9 -431225.4205 271225.4205 False
19 67 -82500.0 0.9 -433725.4205 268725.4205 False
19 68 -62500.0 0.9 -413725.4205 288725.4205 False
19 70 -40000.0 0.9 -326774.355 246774.355 False
19 77 -138333.3333 0.9 -400121.3052 123454.6386 False
19 80 -109850.0 0.9 -396624.355 176924.355 False
19 81 -81000.0 0.9 -432225.4205 270225.4205 False
19 84 -93500.0 0.9 -380274.355 193274.355 False
19 87 -151500.0 0.9 -502725.4205 199725.4205 False
19 90 -133000.0 0.9 -484225.4205 218225.4205 False
19 91 -144000.0 0.9 -495225.4205 207225.4205 False
19 94 -134000.0 0.9 -420774.355 152774.355 False
19 96 -118333.3333 0.9 -352483.6137 115816.947 False
19 98 -117224.0 0.9 -468449.4205 234001.4205 False
19 99 -37100.0 0.9 -277032.6394 202832.6394 False
19 100 -159000.0 0.9 -445774.355 127774.355 False
19 102 -99100.0 0.9 -333250.2803 135050.2803 False
19 105 -101000.0 0.9 -452225.4205 250225.4205 False
19 108 -83333.3333 0.9 -345121.3052 178454.6386 False
19 112 -98532.3913 0.9 -309945.2507 112880.4681 False
19 114 -77000.0 0.9 -316932.6394 162932.6394 False
19 115 -139050.0 0.9 -425824.355 147724.355 False
19 116 -95375.0 0.9 -343728.8766 152978.8766 False
19 120 -90100.0 0.9 -330032.6394 149832.6394 False
19 123 -65000.0 0.9 -416225.4205 286225.4205 False
19 126 -77500.0 0.9 -339287.9719 184287.9719 False
19 128 -111125.0 0.9 -359478.8766 137228.8766 False
19 129 -131000.0 0.9 -482225.4205 220225.4205 False
19 130 -105000.0 0.9 -456225.4205 246225.4205 False
19 134 -92000.0 0.9 -378774.355 194774.355 False
19 136 -92000.0 0.9 -443225.4205 259225.4205 False
19 137 -96000.0 0.9 -382774.355 190774.355 False
19 138 -118000.0 0.9 -404774.355 168774.355 False
19 140 -120000.0 0.9 -471225.4205 231225.4205 False
19 143 -60500.0 0.9 -347274.355 226274.355 False
19 144 -83571.4286 0.9 -313502.4393 146359.5822 False
19 145 -30000.0 0.9 -381225.4205 321225.4205 False
19 148 -112500.0 0.9 -463725.4205 238725.4205 False
19 150 -56300.0 0.9 -296232.6394 183632.6394 False
19 154 -60600.0 0.9 -290531.0108 169331.0108 False
19 156 -75000.0 0.9 -304931.0108 154931.0108 False
19 158 -75533.3333 0.9 -337321.3052 186254.6386 False
19 160 -91000.0 0.9 -352787.9719 170787.9719 False
19 162 115000.0 0.9 -146787.9719 376787.9719 False
19 164 -112520.0 0.9 -352452.6394 127412.6394 False
19 168 -91833.3333 0.9 -353621.3052 169954.6386 False
19 169 -45000.0 0.9 -396225.4205 306225.4205 False
19 170 -6500.0 0.9 -293274.355 280274.355 False
19 172 -180000.0 0.9 -531225.4205 171225.4205 False
19 174 67000.0 0.9 -219774.355 353774.355 False
19 176 -97366.6667 0.9 -359154.6386 164421.3052 False
19 177 -21500.0 0.9 -372725.4205 329725.4205 False
19 180 -61250.0 0.9 -348024.355 225524.355 False
19 183 -68000.0 0.9 -419225.4205 283225.4205 False
19 184 -92666.6667 0.9 -354454.6386 169121.3052 False
19 185 -71000.0 0.9 -319353.8766 177353.8766 False
19 189 -67000.0 0.9 -418225.4205 284225.4205 False
19 190 -110750.0 0.9 -397524.355 176024.355 False
19 192 -42500.0 0.9 -272431.0108 187431.0108 False
19 194 -66100.0 0.9 -417325.4205 285125.4205 False
19 196 -81000.0 0.9 -367774.355 205774.355 False
19 198 -115000.0 0.9 -466225.4205 236225.4205 False
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386 552 -30979.0 0.9 -382204.4205 320246.4205 False
-----------------------------------------------------------------
sns.scatterplot(x = data['EnclosedPorch'], y = data.Property_Sale_Price, data = data )
plt.xlabel('EnclosedPorch')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of EnclosedPorch vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter Plot Of EnclosedPorch vs Property_Sale_Price')
Not Good Predictor
data['ThrSsnPorch'].isnull().sum()
0
data['ThrSsnPorch'].describe()
count 2073.000000 mean 2.979257 std 26.817218 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 508.000000 Name: ThrSsnPorch, dtype: float64
data['ThrSsnPorch'].value_counts()
0 2041 168 6 23 3 304 2 182 2 180 2 216 2 162 2 144 2 196 1 96 1 238 1 290 1 140 1 320 1 508 1 130 1 153 1 245 1 407 1 Name: ThrSsnPorch, dtype: int64
data['ThrSsnPorch'].value_counts().sum()
2073
sns.displot(data['ThrSsnPorch'])
plt.title('Histogram Of ThrSsnPorch')
Text(0.5, 1.0, 'Histogram Of ThrSsnPorch')
sns.distplot(data['ThrSsnPorch'])
plt.title('Normality Of ThrSsnPorch')
Text(0.5, 1.0, 'Normality Of ThrSsnPorch')
sns.countplot(data['ThrSsnPorch'])
plt.title('ThrSsnPorch - count')
Text(0.5, 1.0, 'ThrSsnPorch - count')
sns.boxplot(data['ThrSsnPorch'], color = 'red')
plt.title('BoxPlot Of ThrSsnPorch')
Text(0.5, 1.0, 'BoxPlot Of ThrSsnPorch')
IQR = data['ThrSsnPorch'].quantile(0.75) - data['ThrSsnPorch'].quantile(0.25)
IQR
0.0
UL = data['ThrSsnPorch'].quantile(0.75) + (1.5*IQR)
UL
0.0
LL = data['ThrSsnPorch'].quantile(0.25) - (1.5*IQR)
LL
0.0
np.corrcoef(data['ThrSsnPorch'], data.Property_Sale_Price)
array([[1. , 0.05601521],
[0.05601521, 1. ]])
tukey_EP = pairwise_tukeyhsd(data.Property_Sale_Price, data['ThrSsnPorch'], alpha = 0.05)
print(tukey_EP)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
=================================================================
group1 group2 meandiff p-adj lower upper reject
-----------------------------------------------------------------
0 23 -7166.5546 0.9 -144350.1147 130017.0054 False
0 96 2743.4454 0.9 -234749.1751 240236.0659 False
0 130 2843.4454 0.9 -234649.1751 240336.0659 False
0 140 -42156.5546 0.9 -279649.1751 195336.0659 False
0 144 34343.4454 0.9 -133630.3117 202317.2024 False
0 153 162843.4454 0.6147 -74649.1751 400336.0659 False
0 162 -27656.5546 0.9 -195630.3117 140317.2024 False
0 168 43176.7787 0.9 -53897.8072 140251.3646 False
0 180 -42156.5546 0.9 -210130.3117 125817.2024 False
0 182 44843.4454 0.9 -123130.3117 212817.2024 False
0 196 51343.4454 0.9 -186149.1751 288836.0659 False
0 216 7343.4454 0.9 -160630.3117 175317.2024 False
0 238 17343.4454 0.9 -220149.1751 254836.0659 False
0 245 54343.4454 0.9 -183149.1751 291836.0659 False
0 290 85343.4454 0.9 -152149.1751 322836.0659 False
0 304 162843.4454 0.071 -5130.3117 330817.2024 False
0 320 -34156.5546 0.9 -271649.1751 203336.0659 False
0 407 3343.4454 0.9 -234149.1751 240836.0659 False
0 508 3343.4454 0.9 -234149.1751 240836.0659 False
23 96 9910.0 0.9 -264255.7004 284075.7004 False
23 130 10010.0 0.9 -264155.7004 284175.7004 False
23 140 -34990.0 0.9 -309155.7004 239175.7004 False
23 144 41510.0 0.9 -175237.0174 258257.0174 False
23 153 170010.0 0.768 -104155.7004 444175.7004 False
23 162 -20490.0 0.9 -237237.0174 196257.0174 False
23 168 50343.3333 0.9 -117548.1844 218234.8511 False
23 180 -34990.0 0.9 -251737.0174 181757.0174 False
23 182 52010.0 0.9 -164737.0174 268757.0174 False
23 196 58510.0 0.9 -215655.7004 332675.7004 False
23 216 14510.0 0.9 -202237.0174 231257.0174 False
23 238 24510.0 0.9 -249655.7004 298675.7004 False
23 245 61510.0 0.9 -212655.7004 335675.7004 False
23 290 92510.0 0.9 -181655.7004 366675.7004 False
23 304 170010.0 0.3709 -46737.0174 386757.0174 False
23 320 -26990.0 0.9 -301155.7004 247175.7004 False
23 407 10510.0 0.9 -263655.7004 284675.7004 False
23 508 10510.0 0.9 -263655.7004 284675.7004 False
96 130 100.0 0.9 -335683.0355 335883.0355 False
96 140 -44900.0 0.9 -380683.0355 290883.0355 False
96 144 31600.0 0.9 -259196.6389 322396.6389 False
96 153 160100.0 0.9 -175683.0355 495883.0355 False
96 162 -30400.0 0.9 -321196.6389 260396.6389 False
96 168 40433.3333 0.9 -216025.1962 296891.8629 False
96 180 -44900.0 0.9 -335696.6389 245896.6389 False
96 182 42100.0 0.9 -248696.6389 332896.6389 False
96 196 48600.0 0.9 -287183.0355 384383.0355 False
96 216 4600.0 0.9 -286196.6389 295396.6389 False
96 238 14600.0 0.9 -321183.0355 350383.0355 False
96 245 51600.0 0.9 -284183.0355 387383.0355 False
96 290 82600.0 0.9 -253183.0355 418383.0355 False
96 304 160100.0 0.9 -130696.6389 450896.6389 False
96 320 -36900.0 0.9 -372683.0355 298883.0355 False
96 407 600.0 0.9 -335183.0355 336383.0355 False
96 508 600.0 0.9 -335183.0355 336383.0355 False
130 140 -45000.0 0.9 -380783.0355 290783.0355 False
130 144 31500.0 0.9 -259296.6389 322296.6389 False
130 153 160000.0 0.9 -175783.0355 495783.0355 False
130 162 -30500.0 0.9 -321296.6389 260296.6389 False
130 168 40333.3333 0.9 -216125.1962 296791.8629 False
130 180 -45000.0 0.9 -335796.6389 245796.6389 False
130 182 42000.0 0.9 -248796.6389 332796.6389 False
130 196 48500.0 0.9 -287283.0355 384283.0355 False
130 216 4500.0 0.9 -286296.6389 295296.6389 False
130 238 14500.0 0.9 -321283.0355 350283.0355 False
130 245 51500.0 0.9 -284283.0355 387283.0355 False
130 290 82500.0 0.9 -253283.0355 418283.0355 False
130 304 160000.0 0.9 -130796.6389 450796.6389 False
130 320 -37000.0 0.9 -372783.0355 298783.0355 False
130 407 500.0 0.9 -335283.0355 336283.0355 False
130 508 500.0 0.9 -335283.0355 336283.0355 False
140 144 76500.0 0.9 -214296.6389 367296.6389 False
140 153 205000.0 0.7904 -130783.0355 540783.0355 False
140 162 14500.0 0.9 -276296.6389 305296.6389 False
140 168 85333.3333 0.9 -171125.1962 341791.8629 False
140 180 0.0 0.9 -290796.6389 290796.6389 False
140 182 87000.0 0.9 -203796.6389 377796.6389 False
140 196 93500.0 0.9 -242283.0355 429283.0355 False
140 216 49500.0 0.9 -241296.6389 340296.6389 False
140 238 59500.0 0.9 -276283.0355 395283.0355 False
140 245 96500.0 0.9 -239283.0355 432283.0355 False
140 290 127500.0 0.9 -208283.0355 463283.0355 False
140 304 205000.0 0.5697 -85796.6389 495796.6389 False
140 320 8000.0 0.9 -327783.0355 343783.0355 False
140 407 45500.0 0.9 -290283.0355 381283.0355 False
140 508 45500.0 0.9 -290283.0355 381283.0355 False
144 153 128500.0 0.9 -162296.6389 419296.6389 False
144 162 -62000.0 0.9 -299434.4614 175434.4614 False
144 168 8833.3333 0.9 -185031.0926 202697.7593 False
144 180 -76500.0 0.9 -313934.4614 160934.4614 False
144 182 10500.0 0.9 -226934.4614 247934.4614 False
144 196 17000.0 0.9 -273796.6389 307796.6389 False
144 216 -27000.0 0.9 -264434.4614 210434.4614 False
144 238 -17000.0 0.9 -307796.6389 273796.6389 False
144 245 20000.0 0.9 -270796.6389 310796.6389 False
144 290 51000.0 0.9 -239796.6389 341796.6389 False
144 304 128500.0 0.9 -108934.4614 365934.4614 False
144 320 -68500.0 0.9 -359296.6389 222296.6389 False
144 407 -31000.0 0.9 -321796.6389 259796.6389 False
144 508 -31000.0 0.9 -321796.6389 259796.6389 False
153 162 -190500.0 0.6862 -481296.6389 100296.6389 False
153 168 -119666.6667 0.9 -376125.1962 136791.8629 False
153 180 -205000.0 0.5697 -495796.6389 85796.6389 False
153 182 -118000.0 0.9 -408796.6389 172796.6389 False
153 196 -111500.0 0.9 -447283.0355 224283.0355 False
153 216 -155500.0 0.9 -446296.6389 135296.6389 False
153 238 -145500.0 0.9 -481283.0355 190283.0355 False
153 245 -108500.0 0.9 -444283.0355 227283.0355 False
153 290 -77500.0 0.9 -413283.0355 258283.0355 False
153 304 0.0 0.9 -290796.6389 290796.6389 False
153 320 -197000.0 0.846 -532783.0355 138783.0355 False
153 407 -159500.0 0.9 -495283.0355 176283.0355 False
153 508 -159500.0 0.9 -495283.0355 176283.0355 False
162 168 70833.3333 0.9 -123031.0926 264697.7593 False
162 180 -14500.0 0.9 -251934.4614 222934.4614 False
162 182 72500.0 0.9 -164934.4614 309934.4614 False
162 196 79000.0 0.9 -211796.6389 369796.6389 False
162 216 35000.0 0.9 -202434.4614 272434.4614 False
162 238 45000.0 0.9 -245796.6389 335796.6389 False
162 245 82000.0 0.9 -208796.6389 372796.6389 False
162 290 113000.0 0.9 -177796.6389 403796.6389 False
162 304 190500.0 0.3246 -46934.4614 427934.4614 False
162 320 -6500.0 0.9 -297296.6389 284296.6389 False
162 407 31000.0 0.9 -259796.6389 321796.6389 False
162 508 31000.0 0.9 -259796.6389 321796.6389 False
168 180 -85333.3333 0.9 -279197.7593 108531.0926 False
168 182 1666.6667 0.9 -192197.7593 195531.0926 False
168 196 8166.6667 0.9 -248291.8629 264625.1962 False
168 216 -35833.3333 0.9 -229697.7593 158031.0926 False
168 238 -25833.3333 0.9 -282291.8629 230625.1962 False
168 245 11166.6667 0.9 -245291.8629 267625.1962 False
168 290 42166.6667 0.9 -214291.8629 298625.1962 False
168 304 119666.6667 0.7746 -74197.7593 313531.0926 False
168 320 -77333.3333 0.9 -333791.8629 179125.1962 False
168 407 -39833.3333 0.9 -296291.8629 216625.1962 False
168 508 -39833.3333 0.9 -296291.8629 216625.1962 False
180 182 87000.0 0.9 -150434.4614 324434.4614 False
180 196 93500.0 0.9 -197296.6389 384296.6389 False
180 216 49500.0 0.9 -187934.4614 286934.4614 False
180 238 59500.0 0.9 -231296.6389 350296.6389 False
180 245 96500.0 0.9 -194296.6389 387296.6389 False
180 290 127500.0 0.9 -163296.6389 418296.6389 False
180 304 205000.0 0.1997 -32434.4614 442434.4614 False
180 320 8000.0 0.9 -282796.6389 298796.6389 False
180 407 45500.0 0.9 -245296.6389 336296.6389 False
180 508 45500.0 0.9 -245296.6389 336296.6389 False
182 196 6500.0 0.9 -284296.6389 297296.6389 False
182 216 -37500.0 0.9 -274934.4614 199934.4614 False
182 238 -27500.0 0.9 -318296.6389 263296.6389 False
182 245 9500.0 0.9 -281296.6389 300296.6389 False
182 290 40500.0 0.9 -250296.6389 331296.6389 False
182 304 118000.0 0.9 -119434.4614 355434.4614 False
182 320 -79000.0 0.9 -369796.6389 211796.6389 False
182 407 -41500.0 0.9 -332296.6389 249296.6389 False
182 508 -41500.0 0.9 -332296.6389 249296.6389 False
196 216 -44000.0 0.9 -334796.6389 246796.6389 False
196 238 -34000.0 0.9 -369783.0355 301783.0355 False
196 245 3000.0 0.9 -332783.0355 338783.0355 False
196 290 34000.0 0.9 -301783.0355 369783.0355 False
196 304 111500.0 0.9 -179296.6389 402296.6389 False
196 320 -85500.0 0.9 -421283.0355 250283.0355 False
196 407 -48000.0 0.9 -383783.0355 287783.0355 False
196 508 -48000.0 0.9 -383783.0355 287783.0355 False
216 238 10000.0 0.9 -280796.6389 300796.6389 False
216 245 47000.0 0.9 -243796.6389 337796.6389 False
216 290 78000.0 0.9 -212796.6389 368796.6389 False
216 304 155500.0 0.6866 -81934.4614 392934.4614 False
216 320 -41500.0 0.9 -332296.6389 249296.6389 False
216 407 -4000.0 0.9 -294796.6389 286796.6389 False
216 508 -4000.0 0.9 -294796.6389 286796.6389 False
238 245 37000.0 0.9 -298783.0355 372783.0355 False
238 290 68000.0 0.9 -267783.0355 403783.0355 False
238 304 145500.0 0.9 -145296.6389 436296.6389 False
238 320 -51500.0 0.9 -387283.0355 284283.0355 False
238 407 -14000.0 0.9 -349783.0355 321783.0355 False
238 508 -14000.0 0.9 -349783.0355 321783.0355 False
245 290 31000.0 0.9 -304783.0355 366783.0355 False
245 304 108500.0 0.9 -182296.6389 399296.6389 False
245 320 -88500.0 0.9 -424283.0355 247283.0355 False
245 407 -51000.0 0.9 -386783.0355 284783.0355 False
245 508 -51000.0 0.9 -386783.0355 284783.0355 False
290 304 77500.0 0.9 -213296.6389 368296.6389 False
290 320 -119500.0 0.9 -455283.0355 216283.0355 False
290 407 -82000.0 0.9 -417783.0355 253783.0355 False
290 508 -82000.0 0.9 -417783.0355 253783.0355 False
304 320 -197000.0 0.6339 -487796.6389 93796.6389 False
304 407 -159500.0 0.9 -450296.6389 131296.6389 False
304 508 -159500.0 0.9 -450296.6389 131296.6389 False
320 407 37500.0 0.9 -298283.0355 373283.0355 False
320 508 37500.0 0.9 -298283.0355 373283.0355 False
407 508 0.0 0.9 -335783.0355 335783.0355 False
-----------------------------------------------------------------
sns.scatterplot(x = data['ThrSsnPorch'], y = data.Property_Sale_Price, data = data )
plt.xlabel('ThrSsnPorch')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of ThrSsnPorch vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter Plot Of ThrSsnPorch vs Property_Sale_Price')
Not Good Predictor
data.ScreenPorch.isnull().sum()
0
data.ScreenPorch.describe()
count 2073.000000 mean 14.552340 std 54.099626 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 480.000000 Name: ScreenPorch, dtype: float64
data.ScreenPorch.value_counts()
0 1912
168 8
120 7
192 7
224 6
...
374 1
396 1
410 1
440 1
385 1
Name: ScreenPorch, Length: 76, dtype: int64
data.ScreenPorch.value_counts().sum()
2073
sns.displot(data.ScreenPorch)
plt.title('Histogram Of ScreenPorch')
Text(0.5, 1.0, 'Histogram Of ScreenPorch')
sns.distplot(data.ScreenPorch)
plt.title('Normality Of ScreenPorch')
Text(0.5, 1.0, 'Normality Of ScreenPorch')
sns.countplot(data.ScreenPorch)
plt.title('ScreenPorch - count')
Text(0.5, 1.0, 'ScreenPorch - count')
sns.boxplot(data.ScreenPorch, color = 'red')
plt.title('BoxPlot Of ScreenPorch')
Text(0.5, 1.0, 'BoxPlot Of ScreenPorch')
IQR = data.ScreenPorch.quantile(0.75) - data.ScreenPorch.quantile(0.25)
IQR
0.0
UL = data.ScreenPorch.quantile(0.75) + (1.5*IQR)
UL
0.0
LL = data.ScreenPorch.quantile(0.25) - (1.5*IQR)
LL
0.0
np.corrcoef(data.ScreenPorch, data.Property_Sale_Price)
array([[1. , 0.10780137],
[0.10780137, 1. ]])
sns.scatterplot(x = data['ScreenPorch'], y = data.Property_Sale_Price, data = data )
plt.xlabel('ScreenPorch')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of ScreenPorch vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter Plot Of ScreenPorch vs Property_Sale_Price')
Not Good Predictor
data.PoolArea.isnull().sum()
0
data.PoolArea.describe()
count 2073.000000 mean 2.299083 std 37.411031 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 738.000000 Name: PoolArea, dtype: float64
data.PoolArea.value_counts()
0 2065 738 2 480 1 512 1 576 1 648 1 519 1 555 1 Name: PoolArea, dtype: int64
data.PoolArea.value_counts().sum()
2073
sns.displot(data.PoolArea)
plt.title('Histogram Of PoolArea')
Text(0.5, 1.0, 'Histogram Of PoolArea')
sns.distplot(data.PoolArea)
plt.title('Normality Of PoolArea')
Text(0.5, 1.0, 'Normality Of PoolArea')
sns.countplot(data.PoolArea)
plt.title('PoolArea - count')
Text(0.5, 1.0, 'PoolArea - count')
sns.boxplot(data.PoolArea, color = 'red')
plt.title('BoxPlot Of PoolArea')
Text(0.5, 1.0, 'BoxPlot Of PoolArea')
IQR = data.PoolArea.quantile(0.75) - data.PoolArea.quantile(0.25)
IQR
0.0
UL = data.PoolArea.quantile(0.75) + (1.5*IQR)
UL
0.0
LL = data.PoolArea.quantile(0.25) - (1.5*IQR)
LL
0.0
np.corrcoef(data.PoolArea, data.Property_Sale_Price)
array([[1. , 0.05597028],
[0.05597028, 1. ]])
sns.scatterplot(x = data['PoolArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('PoolArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of PoolArea vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter Plot Of PoolArea vs Property_Sale_Price')
Not Good Predictor
data.PoolQC.isnull().sum()
2065
data.PoolQC.dtype
dtype('O')
data['PoolQC'] = data['PoolQC'].fillna('No Pool')
data.PoolQC.isnull().sum()
0
data.PoolQC.value_counts()
No Pool 2065 Gd 4 Fa 2 Ex 2 Name: PoolQC, dtype: int64
data.PoolQC.value_counts().sum()
2073
sns.countplot(data.PoolQC)
plt.title('PoolQC - count')
Text(0.5, 1.0, 'PoolQC - count')
data.groupby("PoolQC")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| PoolQC | ||||||||
| Ex | 2.0 | 287500.000000 | 74246.212025 | 235000.0 | 261250.0 | 287500.0 | 313750.0 | 340000.0 |
| Fa | 2.0 | 215500.000000 | 48790.367902 | 181000.0 | 198250.0 | 215500.0 | 232750.0 | 250000.0 |
| Gd | 4.0 | 220235.000000 | 63361.873131 | 160000.0 | 168250.0 | 222985.0 | 274970.0 | 274970.0 |
| No Pool | 2065.0 | 177366.807748 | 66960.899857 | 34900.0 | 130000.0 | 163500.0 | 214000.0 | 340000.0 |
mod_PQC = ols('Property_Sale_Price ~ PoolQC', data = data).fit()
sm.stats.anova_lm(mod_PQC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| PoolQC | 3.0 | 3.441199e+10 | 1.147066e+10 | 2.558952 | 0.053479 |
| Residual | 2069.0 | 9.274422e+12 | 4.482563e+09 | NaN | NaN |
tukey_PQC = pairwise_tukeyhsd(data.Property_Sale_Price, data.PoolQC, alpha = 0.05)
print(tukey_PQC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==================================================================
group1 group2 meandiff p-adj lower upper reject
------------------------------------------------------------------
Ex Fa -72000.0 0.6816 -244141.7394 100141.7394 False
Ex Gd -67265.0 0.6341 -216344.1193 81814.1193 False
Ex No Pool -110133.1923 0.0926 -231914.7148 11648.3303 False
Fa Gd 4735.0 0.9 -144344.1193 153814.1193 False
Fa No Pool -38133.1923 0.8333 -159914.7148 83648.3303 False
Gd No Pool -42868.1923 0.5672 -129022.3832 43285.9987 False
------------------------------------------------------------------
Not Good Predictor
data.Fence.isnull().sum()
1669
data.Fence.dtype
dtype('O')
data['Fence'] = data['Fence'].fillna('No Fence')
data.Fence.isnull().sum()
0
data.Fence.value_counts()
No Fence 1669 MnPrv 233 GdPrv 88 GdWo 69 MnWw 14 Name: Fence, dtype: int64
data.Fence.value_counts().sum()
2073
sns.countplot(data.Fence)
plt.title('Fence - count')
Text(0.5, 1.0, 'Fence - count')
data.groupby("Fence")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Fence | ||||||||
| GdPrv | 88.0 | 177929.431818 | 45501.339638 | 108000.0 | 145900.0 | 168000.0 | 192912.5 | 340000.0 |
| GdWo | 69.0 | 134575.115942 | 48914.077933 | 34900.0 | 116000.0 | 133700.0 | 150000.0 | 340000.0 |
| MnPrv | 233.0 | 145314.896996 | 44602.481546 | 40000.0 | 119900.0 | 135000.0 | 157000.0 | 340000.0 |
| MnWw | 14.0 | 136710.714286 | 24206.040080 | 110000.0 | 123475.0 | 127450.0 | 144000.0 | 187000.0 |
| No Fence | 1669.0 | 184202.279209 | 69384.510721 | 35311.0 | 134900.0 | 174000.0 | 225000.0 | 340000.0 |
mod_F = ols('Property_Sale_Price ~ Fence', data = data).fit()
sm.stats.anova_lm(mod_F)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| Fence | 4.0 | 4.667595e+11 | 1.166899e+11 | 27.291632 | 4.190861e-22 |
| Residual | 2068.0 | 8.842075e+12 | 4.275665e+09 | NaN | NaN |
tukey_F = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fence, alpha = 0.05)
print(tukey_F)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- GdPrv GdWo -43354.3159 0.001 -72060.5163 -14648.1154 True GdPrv MnPrv -32614.5348 0.001 -54951.5422 -10277.5274 True GdPrv MnWw -41218.7175 0.1834 -92585.9894 10148.5543 False GdPrv No Fence 6272.8474 0.9 -13252.9215 25798.6163 False GdWo MnPrv 10739.7811 0.7253 -13727.8957 35207.4578 False GdWo MnWw 2135.5983 0.9 -50193.3795 54464.5762 False GdWo No Fence 49627.1633 0.001 27695.8852 71558.4413 True MnPrv MnWw -8604.1827 0.9 -57728.6967 40520.3313 False MnPrv No Fence 38887.3822 0.001 26402.3147 51372.4498 True MnWw No Fence 47491.5649 0.0534 -420.1417 95403.2715 False -----------------------------------------------------------------
data["Fence"].replace('GdPrv', 'No Fence',inplace = True)
data["Fence"].replace('MnWw', 'GdWo',inplace = True)
data.Fence.value_counts()
No Fence 1757 MnPrv 233 GdWo 83 Name: Fence, dtype: int64
data.Fence.value_counts().sum()
2073
mod_F = ols('Property_Sale_Price ~ Fence', data = data).fit()
sm.stats.anova_lm(mod_F)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| Fence | 2.0 | 4.634171e+11 | 2.317086e+11 | 54.224322 | 1.113846e-23 |
| Residual | 2070.0 | 8.845417e+12 | 4.273148e+09 | NaN | NaN |
tukey_F = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fence, alpha = 0.05)
print(tukey_F)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ============================================================== group1 group2 meandiff p-adj lower upper reject -------------------------------------------------------------- GdWo MnPrv 10379.5596 0.4303 -9218.8274 29977.9467 False GdWo No Fence 48952.764 0.001 31730.9942 66174.5338 True MnPrv No Fence 38573.2043 0.001 27883.7264 49262.6822 True --------------------------------------------------------------
data["Fence"].replace('GdWo', 'MnPrv',inplace = True)
data.Fence.value_counts()
No Fence 1757 MnPrv 316 Name: Fence, dtype: int64
data.Fence.value_counts().sum()
2073
F_NF = data[data.Fence == 'No Fence']
F_M = data[data.Fence == 'MnPrv']
ttest_ind(F_NF.Property_Sale_Price, F_M.Property_Sale_Price)
Ttest_indResult(statistic=10.338164977422487, pvalue=1.8417334925793386e-24)
sns.countplot(x = 'Fence', data = data)
plt.title('Fence - count')
Text(0.5, 1.0, 'Fence - count')
sns.catplot(x = 'Fence', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of Fence vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of Fence vs Property_Sale_Price')
sns.boxplot(x = 'Fence', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of Fence vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot Of Fence vs Property_Sale_Price')
Good Predictor
data.MiscFeature.isnull().sum()
1993
data.MiscFeature.dtype
dtype('O')
data['MiscFeature'] = data['MiscFeature'].fillna('Nothing')
data.MiscFeature.isnull().sum()
0
data.MiscFeature.value_counts()
Nothing 1993 Shed 74 Othr 3 Gar2 2 TenC 1 Name: MiscFeature, dtype: int64
data.MiscFeature.value_counts().sum()
2073
sns.countplot(data.MiscFeature)
plt.title('MiscFeature - count')
Text(0.5, 1.0, 'MiscFeature - count')
data.groupby("MiscFeature")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| MiscFeature | ||||||||
| Gar2 | 2.0 | 170750.000000 | 27223.611076 | 151500.0 | 161125.0 | 170750.0 | 180375.0 | 190000.0 |
| Nothing | 1993.0 | 178719.746613 | 67333.487490 | 34900.0 | 130500.0 | 165000.0 | 215000.0 | 340000.0 |
| Othr | 3.0 | 107000.000000 | 45033.320997 | 55000.0 | 94000.0 | 133000.0 | 133000.0 | 133000.0 |
| Shed | 74.0 | 149303.283784 | 51107.030848 | 55993.0 | 114887.5 | 139950.0 | 172000.0 | 277000.0 |
| TenC | 1.0 | 250000.000000 | NaN | 250000.0 | 250000.0 | 250000.0 | 250000.0 | 250000.0 |
mod_MF = ols('Property_Sale_Price ~ MiscFeature', data = data).fit()
sm.stats.anova_lm(mod_MF)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| MiscFeature | 4.0 | 8.203957e+10 | 2.050989e+10 | 4.596879 | 0.001071 |
| Residual | 2068.0 | 9.226795e+12 | 4.461700e+09 | NaN | NaN |
tukey_MF = pairwise_tukeyhsd(data.Property_Sale_Price, data.MiscFeature, alpha = 0.05)
print(tukey_MF)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================== group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------------ Gar2 Nothing 7969.7466 0.9 -121046.0559 136985.5491 False Gar2 Othr -63750.0 0.812 -230225.1758 102725.1758 False Gar2 Shed -21446.7162 0.9 -152128.7978 109235.3654 False Gar2 TenC 79250.0 0.8555 -144099.8858 302599.8858 False Nothing Othr -71719.7466 0.3406 -177087.1062 33647.6129 False Nothing Shed -29416.4628 0.0019 -51005.8758 -7827.0498 True Nothing TenC 71280.2534 0.7999 -111129.9102 253690.417 False Othr Shed 42303.2838 0.795 -65097.8783 149704.4459 False Othr TenC 143000.0 0.3431 -67576.2918 353576.2918 False Shed TenC 100696.7162 0.5556 -82895.759 284289.1914 False ------------------------------------------------------------------
data["MiscFeature"].replace('Gar2', 'Nothing', inplace = True)
data["MiscFeature"].replace('Othr', 'Nothing', inplace = True)
data["MiscFeature"].replace('TenC', 'Nothing', inplace = True)
data.MiscFeature.value_counts()
Nothing 1999 Shed 74 Name: MiscFeature, dtype: int64
data.MiscFeature.value_counts().sum()
2073
MF_N = data[data.MiscFeature == 'Nothing']
MF_S = data[data.MiscFeature == 'Shed']
ttest_ind(MF_N.Property_Sale_Price, MF_S.Property_Sale_Price)
Ttest_indResult(statistic=3.7086072529736223, pvalue=0.00021388527972514962)
sns.countplot(x = 'MiscFeature', data = data)
plt.title('MiscFeature - count')
Text(0.5, 1.0, 'MiscFeature - count')
sns.catplot(x = 'MiscFeature', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of MiscFeature vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of MiscFeature vs Property_Sale_Price')
Good Predictor
data.MiscVal.isnull().sum()
0
data.MiscVal.describe()
count 2073.000000 mean 39.736614 std 429.819512 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 15500.000000 Name: MiscVal, dtype: float64
data.MiscVal.value_counts()
0 1996 400 21 500 11 450 6 700 6 2000 5 600 4 2500 3 1200 3 620 3 480 3 560 2 1300 2 54 1 800 1 1150 1 1400 1 350 1 8300 1 15500 1 3500 1 Name: MiscVal, dtype: int64
data.MiscVal.value_counts().sum()
2073
sns.displot(data.MiscVal)
plt.title('Histogram Of MiscVal')
Text(0.5, 1.0, 'Histogram Of MiscVal')
sns.distplot(data.MiscVal)
plt.title('Normality Of MiscVal')
Text(0.5, 1.0, 'Normality Of MiscVal')
sns.countplot(data.MiscVal)
plt.title('MiscVal - count')
Text(0.5, 1.0, 'MiscVal - count')
sns.boxplot(data.MiscVal, color = 'red')
plt.title('BoxPlot Of MiscVal')
Text(0.5, 1.0, 'BoxPlot Of MiscVal')
IQR = data.MiscVal.quantile(0.75) - data.MiscVal.quantile(0.25)
IQR
0.0
UL = data.MiscVal.quantile(0.75) + (1.5*IQR)
UL
0.0
LL = data.MiscVal.quantile(0.25) - (1.5*IQR)
LL
0.0
np.corrcoef(data.MiscVal, data.Property_Sale_Price)
array([[ 1. , -0.01932011],
[-0.01932011, 1. ]])
mod_cu = ols('Property_Sale_Price ~ MiscVal', data = data).fit()
sm.stats.anova_lm(mod_cu)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| MiscVal | 1.0 | 3.474677e+09 | 3.474677e+09 | 0.773324 | 0.379293 |
| Residual | 2071.0 | 9.305359e+12 | 4.493172e+09 | NaN | NaN |
sns.scatterplot(x = data['MiscVal'], y = data.Property_Sale_Price, data = data )
plt.xlabel('MiscVal')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of MiscVal vs Property_Sale_Price')
Text(0.5, 1.0, 'Scatter Plot Of MiscVal vs Property_Sale_Price')
Not Good Predictor
data.MoSold.isnull().sum()
0
data.MoSold.describe()
count 2073.000000 mean 6.287506 std 2.690130 min 1.000000 25% 5.000000 50% 6.000000 75% 8.000000 max 12.000000 Name: MoSold, dtype: float64
data.MoSold.value_counts()
6 356 7 325 5 294 4 201 8 187 3 140 10 130 11 111 9 87 1 85 2 84 12 73 Name: MoSold, dtype: int64
data.MoSold.value_counts().sum()
2073
sns.countplot(data.MoSold)
plt.title('MoSold - count')
Text(0.5, 1.0, 'MoSold - count')
data.groupby("MoSold")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| MoSold | ||||||||
| 1 | 85.0 | 170737.811765 | 77992.809336 | 39300.0 | 119000.0 | 149000.0 | 216000.0 | 340000.0 |
| 2 | 84.0 | 182012.821429 | 59590.856199 | 93000.0 | 128150.0 | 174250.0 | 214900.0 | 320000.0 |
| 3 | 140.0 | 177742.492857 | 68536.971484 | 52500.0 | 133000.0 | 163200.0 | 200531.0 | 340000.0 |
| 4 | 201.0 | 168179.905473 | 63848.572438 | 68400.0 | 126000.0 | 149700.0 | 197000.0 | 340000.0 |
| 5 | 294.0 | 173787.653061 | 67214.198619 | 37900.0 | 128625.0 | 155450.0 | 205712.5 | 340000.0 |
| 6 | 356.0 | 174163.584270 | 59437.593759 | 55993.0 | 135000.0 | 162950.0 | 206900.0 | 340000.0 |
| 7 | 325.0 | 180120.630769 | 68372.089447 | 40000.0 | 132500.0 | 172500.0 | 210000.0 | 340000.0 |
| 8 | 187.0 | 180640.989305 | 70088.986283 | 64500.0 | 125250.0 | 173900.0 | 239686.0 | 340000.0 |
| 9 | 87.0 | 186318.827586 | 71906.219604 | 67000.0 | 134000.0 | 172000.0 | 230000.0 | 340000.0 |
| 10 | 130.0 | 172409.846154 | 65450.840873 | 35311.0 | 135850.0 | 157500.0 | 196750.0 | 340000.0 |
| 11 | 111.0 | 199417.873874 | 74905.493263 | 34900.0 | 134450.0 | 190000.0 | 245450.0 | 340000.0 |
| 12 | 73.0 | 184742.726027 | 65806.502736 | 62383.0 | 135960.0 | 178000.0 | 235000.0 | 340000.0 |
mod_MS = ols('Property_Sale_Price ~ MoSold', data = data).fit()
sm.stats.anova_lm(mod_MS)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| MoSold | 1.0 | 3.545346e+10 | 3.545346e+10 | 7.917729 | 0.004941 |
| Residual | 2071.0 | 9.273381e+12 | 4.477731e+09 | NaN | NaN |
tukey_MS = pairwise_tukeyhsd(data.Property_Sale_Price, data.MoSold, alpha = 0.05)
print(tukey_MS)
Multiple Comparison of Means - Tukey HSD, FWER=0.05
==============================================================
group1 group2 meandiff p-adj lower upper reject
--------------------------------------------------------------
1 2 11275.0097 0.9 -22368.2411 44918.2604 False
1 3 7004.6811 0.9 -23064.492 37073.8542 False
1 4 -2557.9063 0.9 -30850.9214 25735.1088 False
1 5 3049.8413 0.9 -23880.3962 29980.0788 False
1 6 3425.7725 0.9 -22973.2921 29824.8371 False
1 7 9382.819 0.9 -17257.811 36023.449 False
1 8 9903.1775 0.9 -18702.8724 38509.2275 False
1 9 15581.0158 0.9 -17769.2147 48931.2464 False
1 10 1672.0344 0.9 -28830.9124 32174.9812 False
1 11 28680.0621 0.1164 -2838.0931 60198.2173 False
1 12 14004.9143 0.9 -20889.9374 48899.766 False
2 3 -4270.3286 0.9 -34450.6635 25910.0063 False
2 4 -13832.916 0.9 -42244.0432 14578.2112 False
2 5 -8225.1684 0.9 -35279.4681 18829.1314 False
2 6 -7849.2372 0.9 -34374.8485 18676.3742 False
2 7 -1892.1907 0.9 -28658.2254 24873.844 False
2 8 -1371.8321 0.9 -30094.7069 27351.0427 False
2 9 4306.0062 0.9 -29144.4845 37756.4968 False
2 10 -9602.9753 0.9 -40215.5088 21009.5583 False
2 11 17405.0524 0.7931 -14219.1717 49029.2766 False
2 12 2729.9046 0.9 -32260.7816 37720.5908 False
3 4 -9562.5874 0.9 -33634.9573 14509.7826 False
3 5 -3954.8398 0.9 -26409.7275 18500.0479 False
3 6 -3578.9086 0.9 -25393.9242 18236.107 False
3 7 2378.1379 0.9 -19728.5909 24484.8668 False
3 8 2898.4964 0.9 -21541.0286 27338.0215 False
3 9 8576.3347 0.9 -21277.0096 38429.6791 False
3 10 -5332.6467 0.9 -31967.4746 21302.1812 False
3 11 21675.381 0.3087 -6116.3156 49467.0776 False
3 12 7000.2332 0.9 -24569.2927 38569.7591 False
4 5 5607.7476 0.9 -14406.2854 25621.7806 False
4 6 5983.6788 0.9 -13309.6993 25277.0569 False
4 7 11940.7253 0.6738 -7681.8891 31563.3397 False
4 8 12461.0838 0.7716 -9756.7 34678.8677 False
4 9 18138.9221 0.5961 -9924.6077 46202.4519 False
4 10 4229.9407 0.9 -20382.1212 28842.0026 False
4 11 31237.9684 0.0046 5378.3873 57097.5495 True
4 12 16562.8206 0.7851 -13319.8584 46445.4995 False
5 6 375.9312 0.9 -16857.1018 17608.9642 False
5 7 6332.9777 0.9 -11267.874 23933.8295 False
5 8 6853.3362 0.9 -13600.8297 27307.5022 False
5 9 12531.1745 0.9 -14157.8624 39220.2115 False
5 10 -1377.8069 0.9 -24410.3182 21654.7044 False
5 11 25630.2208 0.0291 1269.1674 49991.2742 True
5 12 10955.073 0.9 -17640.687 39550.8329 False
6 7 5957.0465 0.9 -10819.8097 22733.9027 False
6 8 6477.405 0.9 -13272.1729 26226.983 False
6 9 12155.2433 0.9 -13997.7224 38308.209 False
6 10 -1753.7381 0.9 -24162.8769 20655.4006 False
6 11 25254.2896 0.026 1481.7457 49026.8335 True
6 12 10579.1418 0.9 -17516.9507 38675.2342 False
7 8 520.3585 0.9 -19550.9738 20591.6909 False
7 9 6198.1968 0.9 -20198.5865 32594.9801 False
7 10 -7710.7846 0.9 -30404.0007 14982.4314 False
7 11 19297.2431 0.2656 -4743.2727 43337.7589 False
7 12 4622.0953 0.9 -23701.0925 32945.283 False
8 9 5677.8383 0.9 -22701.2578 34056.9343 False
8 10 -8231.1432 0.9 -33202.4262 16740.1399 False
8 11 18776.8846 0.4487 -7424.82 44978.5891 False
8 12 4101.7367 0.9 -26077.4927 34280.9662 False
9 10 -13908.9814 0.9 -44199.1905 16381.2276 False
9 11 13099.0463 0.9 -18213.2693 44411.3619 False
9 12 -1576.1016 0.9 -36285.1447 33132.9416 False
10 11 27008.0277 0.0773 -1252.4216 55268.477 False
10 12 12332.8799 0.9 -19650.0775 44315.8372 False
11 12 -14675.1478 0.9 -47627.7487 18277.453 False
--------------------------------------------------------------
Not Good Predictor
data.YrSold.isnull().sum()
0
data.YrSold.describe()
count 2073.000000 mean 2007.851905 std 1.329705 min 2006.000000 25% 2007.000000 50% 2008.000000 75% 2009.000000 max 2010.000000 Name: YrSold, dtype: float64
data.YrSold.value_counts()
2009 508 2007 469 2006 428 2008 413 2010 255 Name: YrSold, dtype: int64
data.YrSold.value_counts().sum()
2073
sns.countplot(data.YrSold)
plt.title('YrSold - count')
Text(0.5, 1.0, 'YrSold - count')
data.groupby("YrSold")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| YrSold | ||||||||
| 2006 | 428.0 | 177230.880841 | 67233.883293 | 35311.0 | 130000.0 | 161450.0 | 217927.5 | 340000.0 |
| 2007 | 469.0 | 178468.541578 | 67029.431786 | 39300.0 | 129000.0 | 164000.0 | 211000.0 | 340000.0 |
| 2008 | 413.0 | 178304.188862 | 62525.616601 | 40000.0 | 135500.0 | 172000.0 | 207000.0 | 340000.0 |
| 2009 | 508.0 | 176119.994094 | 68908.466721 | 34900.0 | 127500.0 | 162750.0 | 213062.5 | 340000.0 |
| 2010 | 255.0 | 178369.600000 | 70317.760934 | 55000.0 | 128000.0 | 157900.0 | 214900.0 | 340000.0 |
mod_YS = ols('Property_Sale_Price ~ YrSold', data = data).fit()
sm.stats.anova_lm(mod_YS)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| YrSold | 1.0 | 5.601590e+07 | 5.601590e+07 | 0.012462 | 0.911124 |
| Residual | 2071.0 | 9.308778e+12 | 4.494823e+09 | NaN | NaN |
tukey_YS = pairwise_tukeyhsd(data.Property_Sale_Price, data.YrSold, alpha = 0.05)
print(tukey_YS)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ============================================================ group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------ 2006 2007 1237.6607 0.9 -11005.8613 13481.1828 False 2006 2008 1073.308 0.9 -11560.0714 13706.6875 False 2006 2009 -1110.8867 0.9 -13128.0687 10906.2952 False 2006 2010 1138.7192 0.9 -13350.2281 15627.6664 False 2007 2008 -164.3527 0.9 -12523.5794 12194.874 False 2007 2009 -2348.5475 0.9 -14077.1821 9380.0872 False 2007 2010 -98.9416 0.9 -14349.4784 14151.5952 False 2008 2009 -2184.1948 0.9 -14319.2396 9950.8501 False 2008 2010 65.4111 0.9 -14521.4406 14652.2629 False 2009 2010 2249.6059 0.9 -11806.9452 16306.157 False ------------------------------------------------------------
Not Good Predictor
data.SaleType.isnull().sum()
0
data.SaleType.value_counts()
WD 1797 New 171 COD 68 ConLD 14 CWD 5 ConLI 5 ConLw 5 Oth 5 Con 3 Name: SaleType, dtype: int64
data.SaleType.value_counts().sum()
2073
sns.countplot(data.SaleType)
plt.title('SaleType - count')
Text(0.5, 1.0, 'SaleType - count')
data.groupby("SaleType")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| SaleType | ||||||||
| COD | 68.0 | 155068.382353 | 52786.913875 | 60000.0 | 117500.0 | 139500.0 | 180250.0 | 287000.0 |
| CWD | 5.0 | 234260.000000 | 91654.148842 | 136000.0 | 158000.0 | 219500.0 | 328900.0 | 328900.0 |
| Con | 3.0 | 251466.666667 | 62815.709288 | 215200.0 | 215200.0 | 215200.0 | 269600.0 | 324000.0 |
| ConLD | 14.0 | 130002.000000 | 46876.602601 | 79900.0 | 85000.0 | 132500.0 | 143375.0 | 235128.0 |
| ConLI | 5.0 | 178000.000000 | 97634.522583 | 110000.0 | 115000.0 | 125000.0 | 200000.0 | 340000.0 |
| ConLw | 5.0 | 143700.000000 | 67291.901444 | 60000.0 | 95000.0 | 144000.0 | 207500.0 | 212000.0 |
| New | 171.0 | 248069.853801 | 69113.841065 | 113000.0 | 187125.0 | 239799.0 | 325462.0 | 340000.0 |
| Oth | 5.0 | 131910.000000 | 26022.163630 | 93500.0 | 116050.0 | 150000.0 | 150000.0 | 150000.0 |
| WD | 1797.0 | 172048.427935 | 63147.195319 | 34900.0 | 129000.0 | 159434.0 | 205000.0 | 340000.0 |
mod_ST = ols('Property_Sale_Price ~ SaleType', data = data).fit()
sm.stats.anova_lm(mod_ST)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| SaleType | 8.0 | 1.019415e+12 | 1.274268e+11 | 31.728271 | 2.576475e-47 |
| Residual | 2064.0 | 8.289420e+12 | 4.016192e+09 | NaN | NaN |
tukey_ST = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleType, alpha = 0.05)
print(tukey_ST)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- COD CWD 79191.6176 0.1493 -11987.9012 170371.1365 False COD Con 96398.2843 0.1961 -19690.2733 212486.8419 False COD ConLD -25066.3824 0.9 -82817.9499 32685.1852 False COD ConLI 22931.6176 0.9 -68247.9012 114111.1365 False COD ConLw -11368.3824 0.9 -102547.9012 79811.1365 False COD New 93001.4714 0.001 64790.2582 121212.6847 True COD Oth -23158.3824 0.9 -114337.9012 68021.1365 False COD WD 16980.0456 0.4297 -7330.0266 41290.1177 False CWD Con 17206.6667 0.9 -126499.263 160912.5963 False CWD ConLD -104258.0 0.0428 -206776.7034 -1739.2966 True CWD ConLI -56260.0 0.8986 -180712.9858 68192.9858 False CWD ConLw -90560.0 0.3691 -215012.9858 33892.9858 False CWD New 13809.8538 0.9 -75468.999 103088.7066 False CWD Oth -102350.0 0.2072 -226802.9858 22102.9858 False CWD WD -62211.5721 0.4141 -150335.4656 25912.3215 False Con ConLD -121464.6667 0.0656 -246656.2523 3726.919 False Con ConLI -73466.6667 0.7873 -217172.5963 70239.263 False Con ConLw -107766.6667 0.3251 -251472.5963 35939.263 False Con New -3396.8129 0.9 -117998.5673 111204.9415 False Con Oth -119556.6667 0.194 -263262.5963 24149.263 False Con WD -79418.2387 0.4297 -193122.5446 34286.0671 False ConLD ConLI 47998.0 0.8683 -54520.7034 150516.7034 False ConLD ConLw 13698.0 0.9 -88820.7034 116216.7034 False ConLD New 118067.8538 0.001 63366.3689 172769.3387 True ConLD Oth 1908.0 0.9 -100610.7034 104426.7034 False ConLD WD 42046.4279 0.2457 -10749.0217 94841.8776 False ConLI ConLw -34300.0 0.9 -158752.9858 90152.9858 False ConLI New 70069.8538 0.2644 -19208.999 159348.7066 False ConLI Oth -46090.0 0.9 -170542.9858 78362.9858 False ConLI WD -5951.5721 0.9 -94075.4656 82172.3215 False ConLw New 104369.8538 0.0088 15091.001 193648.7066 True ConLw Oth -11790.0 0.9 -136242.9858 112662.9858 False ConLw WD 28348.4279 0.9 -59775.4656 116472.3215 False New Oth -116159.8538 0.0018 -205438.7066 -26881.001 True New WD -76021.4259 0.001 -91769.0767 -60273.775 True Oth WD 40138.4279 0.8922 -47985.4656 128262.3215 False -----------------------------------------------------------------
data["SaleType"].replace("ConLD", 'COD', inplace = True)
data["SaleType"].replace("ConLI", 'COD', inplace = True)
data["SaleType"].replace("ConLI", 'COD', inplace = True)
data["SaleType"].replace("Oth", 'COD', inplace = True)
data["SaleType"].replace("CWD", 'New', inplace = True)
data["SaleType"].replace("Con", 'New', inplace = True)
data.SaleType.value_counts()
WD 1797 New 179 COD 92 ConLw 5 Name: SaleType, dtype: int64
data.SaleType.value_counts().sum()
2073
mod_ST = ols('Property_Sale_Price ~ SaleType', data = data).fit()
sm.stats.anova_lm(mod_ST)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| SaleType | 3.0 | 1.005686e+12 | 3.352285e+11 | 83.533113 | 5.163998e-51 |
| Residual | 2069.0 | 8.303149e+12 | 4.013122e+09 | NaN | NaN |
tukey_ST = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleType, alpha = 0.05)
print(tukey_ST)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- COD ConLw -7541.6087 0.9 -82336.3581 67253.1407 False COD New 96499.4248 0.001 75605.0983 117393.7513 True COD WD 20806.8192 0.0115 3396.2814 38217.3571 True ConLw New 104041.0335 0.0017 30189.1537 177892.9133 True ConLw WD 28348.4279 0.7243 -44594.3859 101291.2418 False New WD -75692.6056 0.001 -88458.6713 -62926.5398 True ---------------------------------------------------------------
data["SaleType"].replace("ConLw", 'COD', inplace = True)
data.SaleType.value_counts()
WD 1797 New 179 COD 97 Name: SaleType, dtype: int64
data.SaleType.value_counts().sum()
2073
mod_ST = ols('Property_Sale_Price ~ SaleType', data = data).fit()
sm.stats.anova_lm(mod_ST)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| SaleType | 2.0 | 1.005416e+12 | 5.027079e+11 | 125.322537 | 4.209968e-52 |
| Residual | 2070.0 | 8.303418e+12 | 4.011313e+09 | NaN | NaN |
tukey_ST = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleType, alpha = 0.05)
print(tukey_ST)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- COD New 96888.1675 0.001 78159.5701 115616.765 True COD WD 21195.562 0.0038 5711.2049 36679.919 True New WD -75692.6056 0.001 -87335.3674 -64049.8438 True ---------------------------------------------------------------
sns.countplot(x = 'SaleType', data = data)
plt.title('SaleType - count')
Text(0.5, 1.0, 'SaleType - count')
sns.catplot(x = 'SaleType', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of SaleType vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of SaleType vs Property_Sale_Price')
sns.boxplot(x = 'SaleType', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of SaleType vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot Of SaleType vs Property_Sale_Price')
Good Predictor
data.SaleCondition.isnull().sum()
0
data.SaleCondition.value_counts()
Normal 1695 Partial 175 Abnorml 155 Family 24 Alloca 19 AdjLand 5 Name: SaleCondition, dtype: int64
data.SaleCondition.value_counts().sum()
2073
sns.countplot(data.SaleCondition)
plt.title('SaleCondition - count')
Text(0.5, 1.0, 'SaleCondition - count')
data.groupby("SaleCondition")["Property_Sale_Price"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| SaleCondition | ||||||||
| Abnorml | 155.0 | 143199.800000 | 63408.056530 | 34900.0 | 103388.0 | 130000.0 | 174000.0 | 340000.0 |
| AdjLand | 5.0 | 108800.000000 | 24931.405897 | 81000.0 | 82000.0 | 126000.0 | 127500.0 | 127500.0 |
| Alloca | 19.0 | 162420.263158 | 71102.422300 | 55993.0 | 118858.0 | 142953.0 | 189812.0 | 340000.0 |
| Family | 24.0 | 144500.000000 | 45475.028982 | 82500.0 | 108000.0 | 135500.0 | 161375.0 | 259000.0 |
| Normal | 1695.0 | 174529.422419 | 62519.407890 | 39300.0 | 130375.0 | 160000.0 | 207000.0 | 340000.0 |
| Partial | 175.0 | 245874.702857 | 70214.039839 | 113000.0 | 184875.0 | 235128.0 | 325300.0 | 340000.0 |
mod_SC = ols('Property_Sale_Price ~ SaleCondition', data = data).fit()
sm.stats.anova_lm(mod_SC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| SaleCondition | 5.0 | 1.069495e+12 | 2.138990e+11 | 53.660768 | 1.641988e-52 |
| Residual | 2067.0 | 8.239339e+12 | 3.986134e+09 | NaN | NaN |
tukey_SC = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleCondition, alpha = 0.05)
print(tukey_SC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================== group1 group2 meandiff p-adj lower upper reject ------------------------------------------------------------------ Abnorml AdjLand -34399.8 0.8163 -116225.2672 47425.6672 False Abnorml Alloca 19220.4632 0.7856 -24553.0424 62993.9687 False Abnorml Family 1300.2 0.9 -38203.193 40803.593 False Abnorml Normal 31329.6224 0.001 16217.8809 46441.364 True Abnorml Partial 102674.9029 0.001 82811.6007 122538.2051 True AdjLand Alloca 53620.2632 0.5332 -36895.282 144135.8083 False AdjLand Family 35700.0 0.8445 -52829.4425 124229.4425 False AdjLand Normal 65729.4224 0.1844 -14926.0725 146384.9174 False AdjLand Partial 137074.7029 0.001 55395.4833 218753.9224 True Alloca Family -17920.2632 0.9 -73221.0308 37380.5045 False Alloca Normal 12109.1593 0.9 -29436.2566 53654.5751 False Alloca Partial 83454.4397 0.001 39954.9268 126953.9526 True Family Normal 30029.4224 0.1887 -6989.7597 67048.6045 False Family Partial 101374.7029 0.001 62175.138 140574.2677 True Normal Partial 71345.2804 0.001 57046.5894 85643.9714 True ------------------------------------------------------------------
data["SaleCondition"].replace('Family', 'Abnorml', inplace = True)
data["SaleCondition"].replace('Alloca', 'Normal', inplace = True)
data.SaleCondition.value_counts()
Normal 1714 Abnorml 179 Partial 175 AdjLand 5 Name: SaleCondition, dtype: int64
data.SaleCondition.value_counts().sum()
2073
mod_SC = ols('Property_Sale_Price ~ SaleCondition', data = data).fit()
sm.stats.anova_lm(mod_SC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| SaleCondition | 3.0 | 1.066705e+12 | 3.555683e+11 | 89.257366 | 2.580882e-54 |
| Residual | 2069.0 | 8.242129e+12 | 3.983629e+09 | NaN | NaN |
tukey_SC = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleCondition, alpha = 0.05)
print(tukey_SC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 ================================================================= group1 group2 meandiff p-adj lower upper reject ----------------------------------------------------------------- Abnorml AdjLand -34574.1285 0.6071 -108154.142 39005.885 False Abnorml Normal 31021.0617 0.001 18274.1243 43767.9991 True Abnorml Partial 102500.5744 0.001 85249.4042 119751.7446 True AdjLand Normal 65595.1902 0.0936 -7083.9861 138274.3665 False AdjLand Partial 137074.7029 0.001 63471.842 210677.5637 True Normal Partial 71479.5127 0.001 58601.3468 84357.6785 True -----------------------------------------------------------------
data["SaleCondition"].replace('AdjLand', 'Abnorml', inplace = True)
data.SaleCondition.value_counts()
Normal 1714 Abnorml 184 Partial 175 Name: SaleCondition, dtype: int64
data.SaleCondition.value_counts().sum()
2073
mod_SC = ols('Property_Sale_Price ~ SaleCondition', data = data).fit()
sm.stats.anova_lm(mod_SC)
| df | sum_sq | mean_sq | F | PR(>F) | |
|---|---|---|---|---|---|
| SaleCondition | 2.0 | 1.060890e+12 | 5.304452e+11 | 133.126701 | 4.084723e-55 |
| Residual | 2070.0 | 8.247944e+12 | 3.984514e+09 | NaN | NaN |
tukey_SC = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleCondition, alpha = 0.05)
print(tukey_SC)
Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================================== group1 group2 meandiff p-adj lower upper reject --------------------------------------------------------------- Abnorml Normal 31960.5761 0.001 20475.3045 43445.8476 True Abnorml Partial 103440.0887 0.001 87807.6486 119072.5288 True Normal Partial 71479.5127 0.001 59730.5633 83228.462 True ---------------------------------------------------------------
sns.countplot(x = 'SaleCondition', data = data)
plt.title('SaleCondition - count')
Text(0.5, 1.0, 'SaleCondition - count')
sns.catplot(x = 'SaleCondition', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of SaleCondition vs Property_Sale_Price')
Text(0.5, 1.0, 'Catplot Of SaleCondition vs Property_Sale_Price')
sns.boxplot(x = 'SaleCondition', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of SaleCondition vs Property_Sale_Price')
Text(0.5, 1.0, 'Boxplot Of SaleCondition vs Property_Sale_Price')
Good Predictor
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LotFrontage 2073 non-null float64 3 LotArea 2073 non-null float64 4 Road_Type 2073 non-null object 5 Alley 2073 non-null object 6 Property_Shape 2073 non-null object 7 LandContour 2073 non-null object 8 Utilities 2073 non-null object 9 LotConfig 2073 non-null object 10 LandSlope 2073 non-null object 11 Neighborhood 2073 non-null object 12 Condition1 2073 non-null object 13 Condition2 2073 non-null object 14 Dwelling_Type 2073 non-null object 15 HouseStyle 2073 non-null object 16 OverallQual 2073 non-null int64 17 OverallCond 2073 non-null int64 18 YearBuilt 2073 non-null float64 19 YearRemodAdd 2073 non-null int64 20 RoofStyle 2073 non-null object 21 RoofMatl 2073 non-null object 22 Exterior1st 2073 non-null object 23 Exterior2nd 2073 non-null object 24 MasVnrType 2073 non-null object 25 MasVnrArea 2073 non-null float64 26 ExterQual 2073 non-null object 27 ExterCond 2073 non-null object 28 Foundation 2073 non-null object 29 BsmtQual 2073 non-null object 30 BsmtCond 2073 non-null object 31 BsmtExposure 2073 non-null object 32 BsmtFinType1 2073 non-null object 33 BsmtFinSF1 2073 non-null float64 34 BsmtFinType2 2073 non-null object 35 BsmtFinSF2 2073 non-null int64 36 BsmtUnfSF 2073 non-null float64 37 TotalBsmtSF 2073 non-null float64 38 Heating 2073 non-null object 39 HeatingQC 2073 non-null object 40 CentralAir 2073 non-null object 41 Electrical 2073 non-null object 42 FirstFlrSF 2073 non-null float64 43 SecondFlrSF 2073 non-null float64 44 LowQualFinSF 2073 non-null int64 45 GrLivArea 2073 non-null float64 46 BsmtFullBath 2073 non-null int64 47 BsmtHalfBath 2073 non-null int64 48 FullBath 2073 non-null int64 49 HalfBath 2073 non-null int64 50 BedroomAbvGr 2073 non-null int64 51 KitchenAbvGr 2073 non-null int64 52 KitchenQual 2073 non-null object 53 TotRmsAbvGrd 2073 non-null float64 54 Functional 2073 non-null object 55 Fireplaces 2073 non-null int64 56 FireplaceQu 2073 non-null object 57 GarageType 2073 non-null object 58 GarageYrBlt 2073 non-null float64 59 GarageFinish 2073 non-null object 60 GarageCars 2073 non-null int64 61 GarageArea 2073 non-null float64 62 GarageQual 2073 non-null object 63 GarageCond 2073 non-null object 64 PavedDrive 2073 non-null object 65 WoodDeckSF 2073 non-null float64 66 OpenPorchSF 2073 non-null float64 67 EnclosedPorch 2073 non-null int64 68 ThrSsnPorch 2073 non-null int64 69 ScreenPorch 2073 non-null int64 70 PoolArea 2073 non-null int64 71 PoolQC 2073 non-null object 72 Fence 2073 non-null object 73 MiscFeature 2073 non-null object 74 MiscVal 2073 non-null int64 75 MoSold 2073 non-null int64 76 YrSold 2073 non-null int64 77 SaleType 2073 non-null object 78 SaleCondition 2073 non-null object 79 Property_Sale_Price 2073 non-null float64 dtypes: float64(16), int64(21), object(43) memory usage: 1.3+ MB
Dewll_Type
Zone_Class
Road_Type
Alley
Proparty_Shape
LandContour
LotConfig
Neighborhood
Condition1
Condition2
Dwelling_Type
HouseStyle
OverallQual
OverallCond
RoofStyle
RoofMatl
Exterior1st
Exterior2nd
MasVnrType
ExterQual
ExterCond
Foundation
BsmtQual
BsmtCond
BsmtExposure
BsmtFinType1
BsmtFinType2
TotalBsmtSF
Heating
HeatingQC
CentralAir
Electrical
FirstFlrSF
GrLivArea
BsmtFullBath
FullBath
HalfBath
BedroomAbvGr
KitchenAbvGr
KitchenQual
Functional
Fireplaces
FireplaceQu
GarageType*
GarageFinish
GarageCars
GarageArea
GarageQual
GarageCond
PavedDrive
Fence
MiscFeature
SaleType
SaleCondition
LotFrontage
LotArea
Utilities
LandSlope
YearBuilt
YearRemodAdd
MasVnrArea
BsmtFinSF1
BsmtFinSF2
BsmtUnfSF
SecondFlrSF
LowQualFinSF
BsmtHalfBath
TotRmsAbvGrd
GarageYrBlt
WoodDeckSF
OpenPorchSF
EnclosedPorch
ThrSsnPorch
ScreenPorch
PoolArea
PoolQC
MiscVal
MoSold
YrSold
# Heat Map OF Corrrlation
plt.figure(figsize=(18,10))
sns.heatmap(data.corr(), annot=True, cmap='coolwarm')
plt.show()
data.corr()
| Dwell_Type | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | ... | WoodDeckSF | OpenPorchSF | EnclosedPorch | ThrSsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | Property_Sale_Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dwell_Type | 1.000000 | -0.197055 | -0.221013 | 0.204063 | -0.112947 | 0.244180 | 0.184536 | 0.107031 | 0.012007 | -0.060803 | ... | 0.065275 | 0.112024 | -0.066921 | 0.001947 | 0.025754 | -0.018590 | -0.025827 | 0.022833 | -0.017130 | 0.166766 |
| LotFrontage | -0.197055 | 1.000000 | 0.459208 | 0.136993 | -0.004603 | 0.104689 | 0.091662 | 0.161011 | 0.099035 | 0.054406 | ... | 0.086593 | 0.121065 | -0.004154 | 0.049341 | 0.043660 | 0.050256 | -0.000393 | 0.019003 | 0.025141 | 0.320624 |
| LotArea | -0.221013 | 0.459208 | 1.000000 | 0.138956 | -0.004317 | 0.023751 | 0.050505 | 0.094470 | 0.157722 | 0.058947 | ... | 0.135594 | 0.128169 | -0.013629 | 0.044514 | 0.086360 | 0.076916 | 0.045142 | 0.002341 | -0.009246 | 0.351790 |
| OverallQual | 0.204063 | 0.136993 | 0.138956 | 1.000000 | -0.146012 | 0.372474 | 0.404121 | 0.278162 | 0.141918 | -0.055794 | ... | 0.165777 | 0.280570 | -0.082193 | 0.012075 | 0.033267 | 0.053432 | -0.037324 | 0.042652 | -0.037480 | 0.567517 |
| OverallCond | -0.112947 | -0.004603 | -0.004317 | -0.146012 | 1.000000 | -0.289472 | -0.070829 | -0.116898 | -0.035057 | 0.058903 | ... | -0.023133 | -0.064008 | 0.083594 | -0.004907 | 0.046261 | 0.007892 | 0.021853 | -0.036567 | 0.020797 | -0.113095 |
| YearBuilt | 0.244180 | 0.104689 | 0.023751 | 0.372474 | -0.289472 | 1.000000 | 0.536302 | 0.289041 | 0.208221 | -0.027257 | ... | 0.201018 | 0.231899 | -0.322230 | 0.034056 | -0.049196 | 0.005784 | -0.038641 | 0.027915 | -0.006166 | 0.474156 |
| YearRemodAdd | 0.184536 | 0.091662 | 0.050505 | 0.404121 | -0.070829 | 0.536302 | 1.000000 | 0.172496 | 0.098668 | -0.073927 | ... | 0.204242 | 0.267031 | -0.177739 | 0.039945 | -0.036832 | -0.009607 | -0.011749 | 0.033514 | 0.058932 | 0.515421 |
| MasVnrArea | 0.107031 | 0.161011 | 0.094470 | 0.278162 | -0.116898 | 0.289041 | 0.172496 | 1.000000 | 0.216680 | -0.083535 | ... | 0.135462 | 0.191367 | -0.104142 | 0.048766 | 0.057674 | -0.007787 | -0.039190 | -0.009081 | 0.021680 | 0.455032 |
| BsmtFinSF1 | 0.012007 | 0.099035 | 0.157722 | 0.141918 | -0.035057 | 0.208221 | 0.098668 | 0.216680 | 1.000000 | -0.061171 | ... | 0.204612 | 0.092423 | -0.090561 | 0.047776 | 0.050709 | 0.042903 | -0.000925 | -0.010797 | 0.029505 | 0.365625 |
| BsmtFinSF2 | -0.060803 | 0.054406 | 0.058947 | -0.055794 | 0.058903 | -0.027257 | -0.073927 | -0.083535 | -0.061171 | 1.000000 | ... | 0.079243 | -0.015166 | 0.058107 | -0.030341 | 0.099569 | 0.027055 | -0.002205 | -0.016562 | 0.025510 | -0.025886 |
| BsmtUnfSF | -0.010513 | 0.139195 | 0.075106 | 0.225745 | -0.100737 | 0.115699 | 0.195964 | 0.132267 | -0.532016 | -0.207911 | ... | -0.004753 | 0.171626 | -0.010807 | 0.010294 | -0.007496 | -0.023064 | -0.019621 | 0.042242 | -0.053336 | 0.238194 |
| TotalBsmtSF | -0.022728 | 0.282358 | 0.274275 | 0.364247 | -0.123429 | 0.338156 | 0.291746 | 0.346140 | 0.439719 | 0.107745 | ... | 0.244713 | 0.281383 | -0.090762 | 0.050481 | 0.083289 | 0.028186 | -0.023143 | 0.027474 | -0.016406 | 0.642536 |
| FirstFlrSF | -0.096690 | 0.332478 | 0.370870 | 0.312481 | -0.086842 | 0.237625 | 0.229655 | 0.322886 | 0.355373 | 0.094112 | ... | 0.240863 | 0.227898 | -0.068599 | 0.065857 | 0.089705 | 0.069253 | -0.028824 | 0.047161 | 0.003887 | 0.609918 |
| SecondFlrSF | 0.296442 | 0.068540 | 0.093387 | 0.240855 | -0.005713 | 0.014705 | 0.136091 | 0.161980 | -0.141101 | -0.117289 | ... | 0.089103 | 0.208374 | 0.069780 | -0.020816 | 0.059999 | 0.063841 | 0.023607 | 0.044135 | -0.004428 | 0.333837 |
| LowQualFinSF | -0.024857 | 0.021559 | 0.003510 | -0.024440 | 0.002974 | -0.180227 | -0.085126 | -0.081890 | -0.073655 | -0.003688 | ... | -0.019495 | -0.000236 | 0.070810 | -0.006514 | 0.008720 | 0.044597 | 0.003659 | 0.000636 | -0.036017 | -0.060082 |
| GrLivArea | 0.186351 | 0.297422 | 0.344476 | 0.429123 | -0.067653 | 0.175641 | 0.277985 | 0.365599 | 0.121573 | -0.030176 | ... | 0.243360 | 0.353980 | 0.028350 | 0.029899 | 0.114244 | 0.088143 | 0.001175 | 0.075575 | -0.002722 | 0.730869 |
| BsmtFullBath | -0.022725 | -0.073714 | -0.085067 | -0.084245 | 0.046248 | -0.161415 | -0.106546 | -0.098875 | -0.627632 | -0.183154 | ... | -0.160445 | -0.075005 | 0.028235 | -0.025626 | -0.012189 | -0.034299 | 0.028848 | 0.043147 | -0.074749 | -0.232203 |
| BsmtHalfBath | 0.010305 | -0.037997 | 0.025886 | -0.058400 | 0.055967 | -0.017893 | -0.012083 | 0.034106 | 0.084881 | 0.067096 | ... | 0.036247 | -0.025935 | -0.022373 | 0.022508 | 0.050526 | 0.013227 | -0.008855 | 0.015413 | -0.036252 | -0.032733 |
| FullBath | 0.242102 | 0.164903 | 0.179273 | 0.418647 | -0.169708 | 0.402320 | 0.420802 | 0.278219 | 0.065484 | -0.104762 | ... | 0.214795 | 0.307615 | -0.106763 | 0.036172 | -0.031793 | 0.044354 | -0.013396 | 0.062538 | -0.006393 | 0.588944 |
| HalfBath | 0.259017 | 0.054468 | 0.068147 | 0.239253 | -0.040317 | 0.211844 | 0.194911 | 0.192648 | -0.001156 | -0.044654 | ... | 0.099592 | 0.235332 | -0.093901 | -0.004275 | 0.080587 | 0.012495 | -0.042087 | 0.015257 | -0.012208 | 0.328546 |
| BedroomAbvGr | -0.060570 | 0.234971 | 0.273112 | 0.104007 | 0.011237 | -0.009482 | 0.001262 | 0.132469 | -0.055788 | -0.025596 | ... | 0.091328 | 0.091079 | 0.030276 | -0.026118 | 0.062326 | 0.061869 | 0.016255 | 0.050360 | -0.044319 | 0.248098 |
| KitchenAbvGr | 0.025246 | -0.016366 | -0.011894 | -0.147006 | -0.003661 | -0.144694 | -0.149055 | -0.041863 | -0.077816 | -0.045513 | ... | -0.106676 | -0.119048 | 0.034290 | -0.026051 | -0.055131 | -0.014411 | 0.043486 | 0.032244 | 0.022851 | -0.165342 |
| TotRmsAbvGrd | 0.080139 | 0.303711 | 0.324676 | 0.293171 | -0.038508 | 0.090199 | 0.181503 | 0.278722 | -0.014175 | -0.049930 | ... | 0.153887 | 0.240208 | 0.029470 | -0.004556 | 0.054083 | 0.059824 | 0.013393 | 0.035019 | -0.011213 | 0.524436 |
| Fireplaces | 0.106455 | 0.208930 | 0.287976 | 0.269254 | -0.042609 | 0.135261 | 0.105331 | 0.285063 | 0.205123 | 0.037611 | ... | 0.190173 | 0.184315 | 0.001523 | 0.024606 | 0.177940 | 0.071275 | 0.011409 | 0.027378 | 0.004681 | 0.489653 |
| GarageYrBlt | 0.264482 | 0.070730 | 0.009701 | 0.386454 | -0.223018 | 0.683106 | 0.622992 | 0.245647 | 0.130646 | -0.083356 | ... | 0.212194 | 0.280079 | -0.264396 | 0.025085 | -0.086652 | -0.021771 | -0.036614 | 0.005200 | 0.008097 | 0.473168 |
| GarageCars | 0.157532 | 0.273769 | 0.256969 | 0.434882 | -0.144823 | 0.466977 | 0.398475 | 0.373791 | 0.199266 | -0.041077 | ... | 0.226779 | 0.297786 | -0.159327 | 0.042001 | 0.047648 | 0.018949 | -0.045715 | 0.047070 | -0.030055 | 0.668891 |
| GarageArea | 0.072653 | 0.297454 | 0.276975 | 0.401195 | -0.112982 | 0.413012 | 0.342230 | 0.366248 | 0.243708 | -0.018609 | ... | 0.224719 | 0.305243 | -0.115307 | 0.034763 | 0.051474 | 0.031571 | -0.029721 | 0.033485 | -0.017902 | 0.654358 |
| WoodDeckSF | 0.065275 | 0.086593 | 0.135594 | 0.165777 | -0.023133 | 0.201018 | 0.204242 | 0.135462 | 0.204612 | 0.079243 | ... | 1.000000 | 0.097679 | -0.115463 | -0.029845 | -0.081360 | 0.074319 | -0.010760 | 0.028856 | 0.028542 | 0.342547 |
| OpenPorchSF | 0.112024 | 0.121065 | 0.128169 | 0.280570 | -0.064008 | 0.231899 | 0.267031 | 0.191367 | 0.092423 | -0.015166 | ... | 0.097679 | 1.000000 | -0.097845 | -0.005751 | 0.027599 | 0.058725 | -0.029314 | 0.078779 | -0.058424 | 0.426280 |
| EnclosedPorch | -0.066921 | -0.004154 | -0.013629 | -0.082193 | 0.083594 | -0.322230 | -0.177739 | -0.104142 | -0.090561 | 0.058107 | ... | -0.115463 | -0.097845 | 1.000000 | -0.032133 | -0.079499 | 0.039500 | 0.023253 | -0.028394 | 0.018390 | -0.114790 |
| ThrSsnPorch | 0.001947 | 0.049341 | 0.044514 | 0.012075 | -0.004907 | 0.034056 | 0.039945 | 0.048766 | 0.047776 | -0.030341 | ... | -0.029845 | -0.005751 | -0.032133 | 1.000000 | -0.029898 | -0.006831 | -0.000897 | 0.040510 | 0.022259 | 0.056015 |
| ScreenPorch | 0.025754 | 0.043660 | 0.086360 | 0.033267 | 0.046261 | -0.049196 | -0.036832 | 0.057674 | 0.050709 | 0.099569 | ... | -0.081360 | 0.027599 | -0.079499 | -0.029898 | 1.000000 | 0.037916 | 0.024816 | 0.007972 | 0.020460 | 0.107801 |
| PoolArea | -0.018590 | 0.050256 | 0.076916 | 0.053432 | 0.007892 | 0.005784 | -0.009607 | -0.007787 | 0.042903 | 0.027055 | ... | 0.074319 | 0.058725 | 0.039500 | -0.006831 | 0.037916 | 1.000000 | 0.025470 | -0.018915 | -0.059756 | 0.055970 |
| MiscVal | -0.025827 | -0.000393 | 0.045142 | -0.037324 | 0.021853 | -0.038641 | -0.011749 | -0.039190 | -0.000925 | -0.002205 | ... | -0.010760 | -0.029314 | 0.023253 | -0.000897 | 0.024816 | 0.025470 | 1.000000 | -0.004530 | 0.013357 | -0.019320 |
| MoSold | 0.022833 | 0.019003 | 0.002341 | 0.042652 | -0.036567 | 0.027915 | 0.033514 | -0.009081 | -0.010797 | -0.016562 | ... | 0.028856 | 0.078779 | -0.028394 | 0.040510 | 0.007972 | -0.018915 | -0.004530 | 1.000000 | -0.173069 | 0.061714 |
| YrSold | -0.017130 | 0.025141 | -0.009246 | -0.037480 | 0.020797 | -0.006166 | 0.058932 | 0.021680 | 0.029505 | 0.025510 | ... | 0.028542 | -0.058424 | 0.018390 | 0.022259 | 0.020460 | -0.059756 | 0.013357 | -0.173069 | 1.000000 | -0.002453 |
| Property_Sale_Price | 0.166766 | 0.320624 | 0.351790 | 0.567517 | -0.113095 | 0.474156 | 0.515421 | 0.455032 | 0.365625 | -0.025886 | ... | 0.342547 | 0.426280 | -0.114790 | 0.056015 | 0.107801 | 0.055970 | -0.019320 | 0.061714 | -0.002453 | 1.000000 |
37 rows × 37 columns
model1 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LotFrontage + LotArea + Road_Type + Alley + Property_Shape + LandContour + Utilities + LotConfig + LandSlope + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + YearBuilt + YearRemodAdd + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + MasVnrArea + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinSF1 + BsmtFinType2 + BsmtFinSF2 + BsmtUnfSF + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + SecondFlrSF + LowQualFinSF + GrLivArea + BsmtFullBath + BsmtHalfBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + TotRmsAbvGrd + Functional + Fireplaces + FireplaceQu + GarageType + GarageYrBlt + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + WoodDeckSF + OpenPorchSF + EnclosedPorch + ThrSsnPorch + ScreenPorch + PoolArea + PoolQC + Fence + MiscFeature + MiscVal + MoSold + YrSold + SaleType + SaleCondition', data = data).fit()
print(model1.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.925
Model: OLS Adj. R-squared: 0.921
Method: Least Squares F-statistic: 196.3
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:37:22 Log-Likelihood: -23289.
No. Observations: 2073 AIC: 4.683e+04
Df Residuals: 1949 BIC: 4.753e+04
Df Model: 123
Covariance Type: nonrobust
===============================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------------
Intercept -2.922e+05 7.02e+05 -0.416 0.677 -1.67e+06 1.08e+06
Zone_Class[T.FV] 3.004e+04 6890.418 4.360 0.000 1.65e+04 4.36e+04
Zone_Class[T.RL] 3.03e+04 6389.883 4.742 0.000 1.78e+04 4.28e+04
Zone_Class[T.RM] 2.682e+04 6215.736 4.315 0.000 1.46e+04 3.9e+04
Road_Type[T.Pave] 2.266e+04 8013.833 2.827 0.005 6939.181 3.84e+04
Alley[T.No alley access] 5939.6312 2575.209 2.306 0.021 889.178 1.1e+04
Property_Shape[T.Reg] -793.3245 1016.990 -0.780 0.435 -2787.826 1201.177
LandContour[T.HLS] 3527.3205 2988.615 1.180 0.238 -2333.897 9388.538
LandContour[T.Lvl] 2623.6208 2360.162 1.112 0.266 -2005.086 7252.328
Utilities[T.NoSeWa] -4.051e+04 1.24e+04 -3.273 0.001 -6.48e+04 -1.62e+04
LotConfig[T.Inside] -1318.3897 1019.078 -1.294 0.196 -3316.988 680.208
LandSlope[T.Mod] 6570.5172 2445.424 2.687 0.007 1774.595 1.14e+04
LandSlope[T.Sev] -4493.9128 5754.964 -0.781 0.435 -1.58e+04 6792.618
Neighborhood[T.Edwards] -1.016e+04 1951.779 -5.208 0.000 -1.4e+04 -6336.306
Neighborhood[T.IDOTRR] -1.339e+04 3314.098 -4.041 0.000 -1.99e+04 -6894.215
Neighborhood[T.NAmes] -6741.4599 1639.565 -4.112 0.000 -9956.945 -3525.975
Neighborhood[T.NridgHt] 2.524e+04 2133.141 11.832 0.000 2.11e+04 2.94e+04
Neighborhood[T.Somerst] 1.418e+04 1804.307 7.857 0.000 1.06e+04 1.77e+04
Neighborhood[T.StoneBr] 2.344e+04 3268.112 7.171 0.000 1.7e+04 2.98e+04
Condition1[T.Norm] 9638.0329 1599.549 6.025 0.000 6501.027 1.28e+04
Condition1[T.PosN] 1.198e+04 3379.069 3.546 0.000 5353.934 1.86e+04
Condition2[T.Norm] -2039.5224 5218.401 -0.391 0.696 -1.23e+04 8194.711
Condition2[T.PosN] -3.726e+04 1.32e+04 -2.825 0.005 -6.31e+04 -1.14e+04
Dwelling_Type[T.TwnhsE] 8122.1229 2463.600 3.297 0.001 3290.556 1.3e+04
HouseStyle[T.1Story] -353.6546 1792.591 -0.197 0.844 -3869.252 3161.943
HouseStyle[T.2Story] -3521.4454 1849.121 -1.904 0.057 -7147.909 105.018
RoofStyle[T.Hip] 220.5785 1173.048 0.188 0.851 -2079.983 2521.139
RoofMatl[T.WdShngl] 1.513e+04 5297.784 2.856 0.004 4739.051 2.55e+04
Exterior1st[T.MetalSd] -8690.5091 5246.895 -1.656 0.098 -1.9e+04 1599.606
Exterior1st[T.Plywood] -2613.7207 5247.628 -0.498 0.618 -1.29e+04 7677.832
Exterior1st[T.VinylSd] -1.16e+04 5523.238 -2.100 0.036 -2.24e+04 -765.633
Exterior2nd[T.HdBoard] 1825.1634 4011.607 0.455 0.649 -6042.327 9692.654
Exterior2nd[T.MetalSd] 6331.8090 4104.069 1.543 0.123 -1717.017 1.44e+04
Exterior2nd[T.VinylSd] 1.047e+04 4333.800 2.416 0.016 1969.257 1.9e+04
MasVnrType[T.None] -602.3015 1604.159 -0.375 0.707 -3748.349 2543.746
MasVnrType[T.Stone] 3836.7822 1867.175 2.055 0.040 174.912 7498.652
ExterQual[T.Fa] 3344.8323 6918.471 0.483 0.629 -1.02e+04 1.69e+04
ExterQual[T.Gd] -3011.7423 3023.325 -0.996 0.319 -8941.033 2917.549
ExterQual[T.TA] -1.064e+04 3317.651 -3.206 0.001 -1.71e+04 -4129.916
ExterCond[T.TA] -2001.5575 3657.794 -0.547 0.584 -9175.157 5172.042
Foundation[T.CBlock] 1809.9148 1913.567 0.946 0.344 -1942.937 5562.767
Foundation[T.PConc] 3101.6098 2126.787 1.458 0.145 -1069.407 7272.627
BsmtQual[T.Gd] -1.111e+04 2165.086 -5.132 0.000 -1.54e+04 -6864.070
BsmtQual[T.No Basement] -1.113e+04 3947.104 -2.819 0.005 -1.89e+04 -3385.028
BsmtQual[T.TA] -1.113e+04 2605.954 -4.271 0.000 -1.62e+04 -6018.487
BsmtCond[T.No basement] -1.401e+04 3420.880 -4.096 0.000 -2.07e+04 -7304.422
BsmtCond[T.TA] -4356.8336 2059.992 -2.115 0.035 -8396.852 -316.815
BsmtExposure[T.Gd] 8149.2240 1870.512 4.357 0.000 4480.809 1.18e+04
BsmtExposure[T.No] -3009.7047 1131.734 -2.659 0.008 -5229.240 -790.169
BsmtExposure[T.No Basement] -8811.1262 1.36e+04 -0.647 0.518 -3.55e+04 1.79e+04
BsmtFinType1[T.GLQ] 8974.6202 1656.024 5.419 0.000 5726.855 1.22e+04
BsmtFinType1[T.No basement] -3.824e+04 2.68e+04 -1.426 0.154 -9.08e+04 1.44e+04
BsmtFinType1[T.Unf] 2313.7241 1315.470 1.759 0.079 -266.152 4893.600
BsmtFinType2[T.Rec] -7.05e+04 2.22e+04 -3.181 0.001 -1.14e+05 -2.7e+04
BsmtFinType2[T.Unf] -6.615e+04 2.21e+04 -2.990 0.003 -1.1e+05 -2.28e+04
Heating[T.Grav] -1846.8451 5044.417 -0.366 0.714 -1.17e+04 8046.174
HeatingQC[T.Fa] -6829.9815 2995.802 -2.280 0.023 -1.27e+04 -954.669
HeatingQC[T.Gd] -2889.7607 1375.105 -2.101 0.036 -5586.591 -192.930
HeatingQC[T.TA] -3406.4659 1341.600 -2.539 0.011 -6037.587 -775.345
CentralAir[T.Y] 3927.8551 2283.046 1.720 0.086 -549.613 8405.323
Electrical[T.SBrkr] -1917.1380 1823.734 -1.051 0.293 -5493.813 1659.536
KitchenQual[T.Fa] -2.225e+04 4067.091 -5.471 0.000 -3.02e+04 -1.43e+04
KitchenQual[T.Gd] -1.759e+04 2268.898 -7.755 0.000 -2.2e+04 -1.31e+04
KitchenQual[T.TA] -2.228e+04 2533.917 -8.793 0.000 -2.73e+04 -1.73e+04
Functional[T.Typ] 1.767e+04 1947.336 9.072 0.000 1.38e+04 2.15e+04
FireplaceQu[T.Fa] -4642.6378 4851.447 -0.957 0.339 -1.42e+04 4871.933
FireplaceQu[T.Gd] 1857.2395 3731.501 0.498 0.619 -5460.912 9175.391
FireplaceQu[T.No Fireplace] -78.9906 4207.064 -0.019 0.985 -8329.808 8171.827
FireplaceQu[T.TA] 706.9982 3825.204 0.185 0.853 -6794.923 8208.920
GarageType[T.BuiltIn] -1830.6139 2127.683 -0.860 0.390 -6003.387 2342.159
GarageType[T.Detchd] -2210.6164 1429.655 -1.546 0.122 -5014.431 593.198
GarageType[T.No Garage] -7544.8365 6453.830 -1.169 0.243 -2.02e+04 5112.298
GarageFinish[T.No Garage] 2.869e+04 7517.321 3.817 0.000 1.4e+04 4.34e+04
GarageFinish[T.RFn] -4183.3853 1271.275 -3.291 0.001 -6676.587 -1690.184
GarageFinish[T.Unf] -2070.0065 1589.576 -1.302 0.193 -5187.454 1047.441
GarageQual[T.No Garage] -2.048e+04 5838.916 -3.508 0.000 -3.19e+04 -9029.653
GarageQual[T.TA] -1.287e+04 5006.120 -2.570 0.010 -2.27e+04 -3050.233
GarageCond[T.TA] 4495.7326 3304.648 1.360 0.174 -1985.283 1.1e+04
PavedDrive[T.Y] 698.0717 1914.275 0.365 0.715 -3056.169 4452.312
PoolQC[T.Fa] -2.012e+04 2.08e+04 -0.965 0.335 -6.1e+04 2.08e+04
PoolQC[T.Gd] -6.351e+04 1.95e+04 -3.255 0.001 -1.02e+05 -2.52e+04
PoolQC[T.No Pool] 3.921e+05 4.78e+04 8.206 0.000 2.98e+05 4.86e+05
Fence[T.No Fence] -683.9440 1325.728 -0.516 0.606 -3283.939 1916.051
MiscFeature[T.Shed] 4009.3063 2530.370 1.584 0.113 -953.210 8971.823
SaleType[T.New] 2.589e+04 6048.297 4.281 0.000 1.4e+04 3.78e+04
SaleType[T.WD] -1397.2483 2264.712 -0.617 0.537 -5838.760 3044.263
SaleCondition[T.Normal] 9649.0395 1674.752 5.761 0.000 6364.546 1.29e+04
SaleCondition[T.Partial] -1.406e+04 5881.145 -2.391 0.017 -2.56e+04 -2525.705
Dwell_Type -88.7100 21.591 -4.109 0.000 -131.054 -46.366
LotFrontage 19.2300 29.319 0.656 0.512 -38.270 76.730
LotArea 0.4907 0.157 3.130 0.002 0.183 0.798
OverallQual 1979.5705 475.266 4.165 0.000 1047.488 2911.653
OverallCond 2050.1630 815.184 2.515 0.012 451.438 3648.888
YearBuilt 4.4822 25.090 0.179 0.858 -44.724 53.689
YearRemodAdd 188.6820 33.569 5.621 0.000 122.847 254.517
MasVnrArea 6.8752 5.882 1.169 0.243 -4.661 18.411
BsmtFinSF1 -42.4461 9.412 -4.510 0.000 -60.905 -23.987
BsmtFinSF2 -45.2815 9.932 -4.559 0.000 -64.759 -25.804
BsmtUnfSF -52.7726 9.554 -5.524 0.000 -71.509 -34.036
TotalBsmtSF 74.3071 9.902 7.504 0.000 54.887 93.727
FirstFlrSF -15.9138 7.666 -2.076 0.038 -30.948 -0.880
SecondFlrSF -5.6401 6.947 -0.812 0.417 -19.265 7.984
LowQualFinSF -47.8284 11.111 -4.305 0.000 -69.619 -26.038
GrLivArea 55.9784 7.291 7.678 0.000 41.680 70.277
BsmtFullBath -1139.2707 648.018 -1.758 0.079 -2410.151 131.610
BsmtHalfBath 558.9403 1979.837 0.282 0.778 -3323.881 4441.762
FullBath 2735.1900 1428.585 1.915 0.056 -66.524 5536.904
HalfBath 1944.8002 1381.014 1.408 0.159 -763.620 4653.221
BedroomAbvGr 40.5214 915.816 0.044 0.965 -1755.561 1836.604
KitchenAbvGr -1.866e+04 3311.227 -5.635 0.000 -2.52e+04 -1.22e+04
TotRmsAbvGrd 404.1464 598.096 0.676 0.499 -768.829 1577.122
Fireplaces 4734.3514 1693.539 2.796 0.005 1413.012 8055.690
GarageYrBlt -139.2414 36.092 -3.858 0.000 -210.025 -68.458
GarageCars 3100.7897 1535.764 2.019 0.044 88.878 6112.702
GarageArea 34.3182 5.243 6.546 0.000 24.036 44.600
WoodDeckSF 17.7305 4.225 4.196 0.000 9.444 26.017
OpenPorchSF 26.1744 9.890 2.647 0.008 6.778 45.570
EnclosedPorch 6.5234 8.157 0.800 0.424 -9.475 22.522
ThrSsnPorch 21.1005 16.094 1.311 0.190 -10.463 52.664
ScreenPorch 35.7903 8.567 4.178 0.000 18.989 52.592
PoolArea 698.0588 84.368 8.274 0.000 532.597 863.520
MiscVal 1.1816 1.054 1.121 0.263 -0.886 3.249
MoSold 119.1195 164.484 0.724 0.469 -203.463 441.702
YrSold -70.6403 347.145 -0.203 0.839 -751.454 610.173
==============================================================================
Omnibus: 179.506 Durbin-Watson: 2.049
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1055.473
Skew: -0.129 Prob(JB): 6.41e-230
Kurtosis: 6.486 Cond. No. 1.91e+07
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.91e+07. This might indicate that there are
strong multicollinearity or other numerical problems.
model2 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + Road_Type + Alley + Property_Shape + LandContour + LotConfig + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinType2 + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + GrLivArea + BsmtFullBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + Functional + Fireplaces + FireplaceQu + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + Fence + MiscFeature + SaleType + SaleCondition', data = data).fit()
print(model2.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.915
Model: OLS Adj. R-squared: 0.911
Method: Least Squares F-statistic: 222.9
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:37:23 Log-Likelihood: -23428.
No. Observations: 2073 AIC: 4.705e+04
Df Residuals: 1977 BIC: 4.759e+04
Df Model: 95
Covariance Type: nonrobust
===============================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------------
Intercept 2.949e+04 2.73e+04 1.081 0.280 -2.4e+04 8.3e+04
Zone_Class[T.FV] 3.107e+04 7231.259 4.297 0.000 1.69e+04 4.53e+04
Zone_Class[T.RL] 3.11e+04 6714.572 4.632 0.000 1.79e+04 4.43e+04
Zone_Class[T.RM] 2.663e+04 6525.447 4.080 0.000 1.38e+04 3.94e+04
Road_Type[T.Pave] 1.35e+04 8354.476 1.616 0.106 -2880.769 2.99e+04
Alley[T.No alley access] 3874.4335 2689.289 1.441 0.150 -1399.704 9148.571
Property_Shape[T.Reg] -1375.5514 1063.990 -1.293 0.196 -3462.211 711.108
LandContour[T.HLS] 8915.2545 3073.450 2.901 0.004 2887.714 1.49e+04
LandContour[T.Lvl] 4916.7337 2411.037 2.039 0.042 188.293 9645.175
LotConfig[T.Inside] -1480.8200 1057.304 -1.401 0.162 -3554.367 592.727
Neighborhood[T.Edwards] -8284.9469 2013.304 -4.115 0.000 -1.22e+04 -4336.526
Neighborhood[T.IDOTRR] -1.371e+04 3458.817 -3.965 0.000 -2.05e+04 -6930.046
Neighborhood[T.NAmes] -6008.2102 1709.653 -3.514 0.000 -9361.121 -2655.299
Neighborhood[T.NridgHt] 2.789e+04 2169.062 12.860 0.000 2.36e+04 3.21e+04
Neighborhood[T.Somerst] 1.499e+04 1880.285 7.974 0.000 1.13e+04 1.87e+04
Neighborhood[T.StoneBr] 2.644e+04 3369.340 7.848 0.000 1.98e+04 3.31e+04
Condition1[T.Norm] 1.064e+04 1681.972 6.325 0.000 7340.458 1.39e+04
Condition1[T.PosN] 1.492e+04 3540.865 4.212 0.000 7970.970 2.19e+04
Condition2[T.Norm] -49.4815 5460.027 -0.009 0.993 -1.08e+04 1.07e+04
Condition2[T.PosN] -4.295e+04 1.35e+04 -3.180 0.001 -6.94e+04 -1.65e+04
Dwelling_Type[T.TwnhsE] 1.024e+04 2553.144 4.012 0.000 5236.497 1.53e+04
HouseStyle[T.1Story] 619.3548 1873.886 0.331 0.741 -3055.643 4294.353
HouseStyle[T.2Story] -3780.8137 1917.995 -1.971 0.049 -7542.318 -19.309
RoofStyle[T.Hip] 1690.4723 1226.274 1.379 0.168 -714.453 4095.398
RoofMatl[T.WdShngl] 1.042e+04 5474.374 1.904 0.057 -312.074 2.12e+04
Exterior1st[T.MetalSd] -8235.5834 5535.797 -1.488 0.137 -1.91e+04 2621.026
Exterior1st[T.Plywood] -1159.7488 5527.049 -0.210 0.834 -1.2e+04 9679.705
Exterior1st[T.VinylSd] -8741.0645 5810.747 -1.504 0.133 -2.01e+04 2654.767
Exterior2nd[T.HdBoard] 3443.8812 4219.797 0.816 0.415 -4831.835 1.17e+04
Exterior2nd[T.MetalSd] 8510.7359 4316.030 1.972 0.049 46.290 1.7e+04
Exterior2nd[T.VinylSd] 1.087e+04 4550.212 2.389 0.017 1944.769 1.98e+04
MasVnrType[T.None] -1274.2169 1183.501 -1.077 0.282 -3595.257 1046.823
MasVnrType[T.Stone] 3305.9509 1947.921 1.697 0.090 -514.242 7126.144
ExterQual[T.Fa] -3828.9733 7220.285 -0.530 0.596 -1.8e+04 1.03e+04
ExterQual[T.Gd] -4239.2877 3141.431 -1.349 0.177 -1.04e+04 1921.577
ExterQual[T.TA] -1.089e+04 3457.092 -3.149 0.002 -1.77e+04 -4107.815
ExterCond[T.TA] 238.0023 3845.841 0.062 0.951 -7304.326 7780.330
Foundation[T.CBlock] 2437.3976 1976.318 1.233 0.218 -1438.487 6313.282
Foundation[T.PConc] 1020.8542 2194.331 0.465 0.642 -3282.591 5324.299
BsmtQual[T.Gd] -1.285e+04 2265.686 -5.672 0.000 -1.73e+04 -8408.576
BsmtQual[T.No Basement] -1.389e+04 4103.430 -3.386 0.001 -2.19e+04 -5845.956
BsmtQual[T.TA] -1.432e+04 2692.971 -5.319 0.000 -1.96e+04 -9043.355
BsmtCond[T.No basement] -1.469e+04 3564.657 -4.121 0.000 -2.17e+04 -7697.860
BsmtCond[T.TA] -5513.2068 2137.403 -2.579 0.010 -9705.006 -1321.407
BsmtExposure[T.Gd] 9462.7377 1933.198 4.895 0.000 5671.417 1.33e+04
BsmtExposure[T.No] -4748.1988 1174.409 -4.043 0.000 -7051.408 -2444.990
BsmtExposure[T.No Basement] -9863.3394 1.44e+04 -0.686 0.493 -3.8e+04 1.83e+04
BsmtFinType1[T.GLQ] 9513.3563 1668.536 5.702 0.000 6241.083 1.28e+04
BsmtFinType1[T.No basement] 1.91e+04 2.56e+04 0.746 0.456 -3.11e+04 6.93e+04
BsmtFinType1[T.Unf] 857.2812 1287.577 0.666 0.506 -1667.870 3382.432
BsmtFinType2[T.Rec] -1.092e+04 2.04e+04 -0.537 0.592 -5.08e+04 2.9e+04
BsmtFinType2[T.Unf] -7507.2070 2.03e+04 -0.370 0.711 -4.73e+04 3.23e+04
Heating[T.Grav] -2326.7430 5322.299 -0.437 0.662 -1.28e+04 8111.161
HeatingQC[T.Fa] -5947.4515 3109.931 -1.912 0.056 -1.2e+04 151.635
HeatingQC[T.Gd] -1932.1179 1437.285 -1.344 0.179 -4750.870 886.634
HeatingQC[T.TA] -3562.4604 1397.771 -2.549 0.011 -6303.720 -821.201
CentralAir[T.Y] 3923.9751 2401.788 1.634 0.102 -786.326 8634.276
Electrical[T.SBrkr] 588.2151 1876.008 0.314 0.754 -3090.945 4267.375
KitchenQual[T.Fa] -2.619e+04 4234.564 -6.185 0.000 -3.45e+04 -1.79e+04
KitchenQual[T.Gd] -1.773e+04 2389.152 -7.423 0.000 -2.24e+04 -1.3e+04
KitchenQual[T.TA] -2.512e+04 2642.193 -9.507 0.000 -3.03e+04 -1.99e+04
Functional[T.Typ] 1.663e+04 2042.323 8.142 0.000 1.26e+04 2.06e+04
FireplaceQu[T.Fa] -2555.9064 5074.945 -0.504 0.615 -1.25e+04 7396.897
FireplaceQu[T.Gd] 1823.0485 3874.968 0.470 0.638 -5776.402 9422.499
FireplaceQu[T.No Fireplace] -1014.8925 4399.602 -0.231 0.818 -9643.237 7613.452
FireplaceQu[T.TA] 1147.8511 3991.360 0.288 0.774 -6679.864 8975.566
GarageType[T.BuiltIn] -2130.6148 2225.456 -0.957 0.338 -6495.101 2233.871
GarageType[T.Detchd] -4455.8961 1470.281 -3.031 0.002 -7339.360 -1572.432
GarageType[T.No Garage] -2759.4344 6761.041 -0.408 0.683 -1.6e+04 1.05e+04
GarageFinish[T.No Garage] 1.732e+04 7645.305 2.265 0.024 2325.558 3.23e+04
GarageFinish[T.RFn] -3942.0785 1325.972 -2.973 0.003 -6542.527 -1341.630
GarageFinish[T.Unf] -1393.2860 1668.825 -0.835 0.404 -4666.127 1879.554
GarageQual[T.No Garage] -2.498e+04 5863.492 -4.261 0.000 -3.65e+04 -1.35e+04
GarageQual[T.TA] -1.95e+04 4999.433 -3.901 0.000 -2.93e+04 -9696.464
GarageCond[T.TA] 4465.7450 3468.501 1.288 0.198 -2336.557 1.13e+04
PavedDrive[T.Y] -881.8291 1995.314 -0.442 0.659 -4794.969 3031.311
Fence[T.No Fence] -1201.9158 1384.764 -0.868 0.386 -3917.666 1513.834
MiscFeature[T.Shed] 4254.3337 2529.973 1.682 0.093 -707.359 9216.027
SaleType[T.New] 2.814e+04 6361.898 4.424 0.000 1.57e+04 4.06e+04
SaleType[T.WD] 1101.1544 2338.566 0.471 0.638 -3485.158 5687.467
SaleCondition[T.Normal] 9558.9590 1739.497 5.495 0.000 6147.519 1.3e+04
SaleCondition[T.Partial] -1.499e+04 6192.042 -2.420 0.016 -2.71e+04 -2842.286
Dwell_Type -117.1537 21.813 -5.371 0.000 -159.932 -74.375
OverallQual 1368.3789 496.475 2.756 0.006 394.709 2342.048
OverallCond 2952.1808 833.812 3.541 0.000 1316.939 4587.422
TotalBsmtSF 23.0983 2.929 7.887 0.000 17.354 28.842
FirstFlrSF -8.1891 4.149 -1.974 0.049 -16.326 -0.052
GrLivArea 53.0754 2.807 18.907 0.000 47.570 58.581
BsmtFullBath -3036.6573 557.987 -5.442 0.000 -4130.961 -1942.353
FullBath 2717.4349 1476.396 1.841 0.066 -178.020 5612.890
HalfBath 3908.6039 1417.090 2.758 0.006 1129.458 6687.750
BedroomAbvGr 503.5063 926.248 0.544 0.587 -1313.018 2320.030
KitchenAbvGr -1.662e+04 3410.508 -4.875 0.000 -2.33e+04 -9936.000
Fireplaces 4498.6900 1744.577 2.579 0.010 1077.287 7920.093
GarageCars 4183.3804 1600.502 2.614 0.009 1044.533 7322.227
GarageArea 28.4207 5.282 5.381 0.000 18.062 38.780
==============================================================================
Omnibus: 330.370 Durbin-Watson: 2.036
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2963.683
Skew: -0.465 Prob(JB): 0.00
Kurtosis: 8.783 Cond. No. 2.23e+05
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.23e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
Condition2
data.Condition2.value_counts()
Norm 2054 Feedr 16 PosN 3 Name: Condition2, dtype: int64
data["Condition2"].replace('Norm', 'Feedr', inplace = True)
data.Condition2.value_counts()
Feedr 2070 PosN 3 Name: Condition2, dtype: int64
HouseStyle
data.HouseStyle.value_counts()
1Story 1125 2Story 648 1.5Fin 300 Name: HouseStyle, dtype: int64
data["HouseStyle"].replace('1Story', '1.5Fin', inplace = True)
data.HouseStyle.value_counts()
1.5Fin 1425 2Story 648 Name: HouseStyle, dtype: int64
Exterior2nd
data.Exterior2nd.value_counts()
VinylSd 864 HdBoard 581 MetalSd 576 AsbShng 52 Name: Exterior2nd, dtype: int64
data["Exterior2nd"].replace('HdBoard', 'AsbShng', inplace = True)
data["Exterior2nd"].replace('MetalSd', 'AsbShng', inplace = True)
data.Exterior2nd.value_counts()
AsbShng 1209 VinylSd 864 Name: Exterior2nd, dtype: int64
ExterQual
data.ExterQual.value_counts()
TA 1272 Gd 706 Ex 76 Fa 19 Name: ExterQual, dtype: int64
data["ExterQual"].replace('Gd', 'Ex', inplace = True)
data["ExterQual"].replace('Fa', 'Ex', inplace = True)
data.ExterQual.value_counts()
TA 1272 Ex 801 Name: ExterQual, dtype: int64
BsmtExposure
data.BsmtExposure.value_counts()
No 1349 Av 482 Gd 181 No Basement 61 Name: BsmtExposure, dtype: int64
data["BsmtExposure"].replace('No Basement', 'Av', inplace = True)
data.BsmtExposure.value_counts()
No 1349 Av 543 Gd 181 Name: BsmtExposure, dtype: int64
BsmtFinType1
data.BsmtFinType1.value_counts()
Unf 923 GLQ 599 BLQ 492 No basement 59 Name: BsmtFinType1, dtype: int64
data["BsmtFinType1"].replace('Unf', 'BLQ', inplace = True)
data["BsmtFinType1"].replace('No basement', 'BLQ', inplace = True)
data.BsmtFinType1.value_counts()
BLQ 1474 GLQ 599 Name: BsmtFinType1, dtype: int64
HeatingQC
data.HeatingQC.value_counts()
Ex 1045 TA 618 Gd 341 Fa 69 Name: HeatingQC, dtype: int64
data["HeatingQC"].replace('Gd', 'Ex', inplace = True)
data["HeatingQC"].replace('Fa', 'Ex', inplace = True)
data.HeatingQC.value_counts()
Ex 1455 TA 618 Name: HeatingQC, dtype: int64
GarageType
data.GarageType.value_counts()
Attchd 1252 Detchd 572 No Garage 125 BuiltIn 124 Name: GarageType, dtype: int64
data["GarageType"].replace('No Garage', 'Attchd', inplace = True)
data["GarageType"].replace('BuiltIn', 'Attchd', inplace = True)
data.GarageType.value_counts()
Attchd 1501 Detchd 572 Name: GarageType, dtype: int64
GarageFinish
data.GarageFinish.value_counts()
Unf 857 RFn 605 Fin 498 No Garage 113 Name: GarageFinish, dtype: int64
data["GarageFinish"].replace('Unf', 'Fin', inplace = True)
data.GarageFinish.value_counts()
Fin 1355 RFn 605 No Garage 113 Name: GarageFinish, dtype: int64
SaleType
data.SaleType.value_counts()
WD 1797 New 179 COD 97 Name: SaleType, dtype: int64
data["SaleType"].replace('WD', 'COD', inplace = True)
data.SaleType.value_counts()
COD 1894 New 179 Name: SaleType, dtype: int64
model3 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + HeatingQC + FirstFlrSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition', data = data).fit()
print(model3.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.910
Model: OLS Adj. R-squared: 0.908
Method: Least Squares F-statistic: 402.8
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:37:24 Log-Likelihood: -23477.
No. Observations: 2073 AIC: 4.706e+04
Df Residuals: 2021 BIC: 4.735e+04
Df Model: 51
Covariance Type: nonrobust
=============================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------------
Intercept 3.879e+04 1.26e+04 3.074 0.002 1.4e+04 6.35e+04
Zone_Class[T.FV] 3.082e+04 6880.951 4.479 0.000 1.73e+04 4.43e+04
Zone_Class[T.RL] 2.999e+04 6363.279 4.713 0.000 1.75e+04 4.25e+04
Zone_Class[T.RM] 2.419e+04 6170.488 3.921 0.000 1.21e+04 3.63e+04
LandContour[T.HLS] 1.054e+04 3002.668 3.512 0.000 4655.454 1.64e+04
LandContour[T.Lvl] 6457.8894 2291.531 2.818 0.005 1963.881 1.1e+04
Neighborhood[T.Edwards] -8833.5988 1914.853 -4.613 0.000 -1.26e+04 -5078.307
Neighborhood[T.IDOTRR] -1.64e+04 3322.017 -4.938 0.000 -2.29e+04 -9889.062
Neighborhood[T.NAmes] -6107.4222 1646.988 -3.708 0.000 -9337.394 -2877.451
Neighborhood[T.NridgHt] 2.855e+04 2125.881 13.432 0.000 2.44e+04 3.27e+04
Neighborhood[T.Somerst] 1.519e+04 1828.413 8.309 0.000 1.16e+04 1.88e+04
Neighborhood[T.StoneBr] 2.597e+04 3329.171 7.801 0.000 1.94e+04 3.25e+04
Condition1[T.Norm] 1.004e+04 1630.728 6.157 0.000 6842.233 1.32e+04
Condition1[T.PosN] 1.423e+04 3442.680 4.134 0.000 7481.969 2.1e+04
Condition2[T.PosN] -3.878e+04 1.23e+04 -3.159 0.002 -6.29e+04 -1.47e+04
Dwelling_Type[T.TwnhsE] 9805.8697 2394.187 4.096 0.000 5110.538 1.45e+04
HouseStyle[T.2Story] -4091.2054 1815.945 -2.253 0.024 -7652.524 -529.887
Exterior2nd[T.VinylSd] 4746.5916 1185.390 4.004 0.000 2421.877 7071.306
ExterQual[T.TA] -7206.7484 1552.068 -4.643 0.000 -1.03e+04 -4162.929
BsmtQual[T.Gd] -1.478e+04 2130.736 -6.936 0.000 -1.9e+04 -1.06e+04
BsmtQual[T.No Basement] -1.262e+04 3789.732 -3.329 0.001 -2e+04 -5183.596
BsmtQual[T.TA] -1.733e+04 2516.482 -6.885 0.000 -2.23e+04 -1.24e+04
BsmtCond[T.No basement] -1.338e+04 3330.947 -4.016 0.000 -1.99e+04 -6843.759
BsmtCond[T.TA] -5676.5872 2128.857 -2.666 0.008 -9851.572 -1501.603
BsmtExposure[T.Gd] 9679.7799 1909.610 5.069 0.000 5934.771 1.34e+04
BsmtExposure[T.No] -4900.3797 1139.367 -4.301 0.000 -7134.835 -2665.924
BsmtFinType1[T.GLQ] 8559.1031 1310.466 6.531 0.000 5989.097 1.11e+04
HeatingQC[T.TA] -2222.2766 1163.699 -1.910 0.056 -4504.451 59.898
KitchenQual[T.Fa] -2.978e+04 3955.811 -7.528 0.000 -3.75e+04 -2.2e+04
KitchenQual[T.Gd] -1.916e+04 2200.817 -8.708 0.000 -2.35e+04 -1.48e+04
KitchenQual[T.TA] -2.699e+04 2487.125 -10.854 0.000 -3.19e+04 -2.21e+04
Functional[T.Typ] 1.753e+04 1983.331 8.837 0.000 1.36e+04 2.14e+04
GarageType[T.Detchd] -5980.0935 1353.428 -4.418 0.000 -8634.353 -3325.834
GarageFinish[T.No Garage] 1.46e+04 3657.111 3.992 0.000 7427.597 2.18e+04
GarageFinish[T.RFn] -3372.8364 1115.401 -3.024 0.003 -5560.293 -1185.380
GarageQual[T.No Garage] -2.683e+04 5625.184 -4.770 0.000 -3.79e+04 -1.58e+04
GarageQual[T.TA] -1.751e+04 4980.624 -3.515 0.000 -2.73e+04 -7737.901
SaleType[T.New] 2.543e+04 6052.840 4.201 0.000 1.36e+04 3.73e+04
SaleCondition[T.Normal] 9849.6247 1643.641 5.993 0.000 6626.217 1.31e+04
SaleCondition[T.Partial] -1.296e+04 6207.761 -2.088 0.037 -2.51e+04 -787.892
Dwell_Type -112.6993 20.748 -5.432 0.000 -153.389 -72.010
OverallQual 1534.7792 496.069 3.094 0.002 561.919 2507.639
OverallCond 3260.4398 821.582 3.968 0.000 1649.204 4871.676
TotalBsmtSF 19.3468 2.543 7.608 0.000 14.360 24.334
FirstFlrSF -1.2126 3.422 -0.354 0.723 -7.923 5.498
GrLivArea 53.3255 2.096 25.442 0.000 49.215 57.436
BsmtFullBath -2809.4724 543.779 -5.167 0.000 -3875.898 -1743.047
HalfBath 3570.6588 1326.939 2.691 0.007 968.348 6172.970
KitchenAbvGr -1.604e+04 3254.343 -4.930 0.000 -2.24e+04 -9660.974
Fireplaces 6721.0056 899.675 7.470 0.000 4956.618 8485.394
GarageCars 4702.5459 1571.593 2.992 0.003 1620.433 7784.658
GarageArea 27.2543 5.244 5.198 0.000 16.971 37.538
==============================================================================
Omnibus: 307.092 Durbin-Watson: 2.034
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2460.583
Skew: -0.444 Prob(JB): 0.00
Kurtosis: 8.263 Cond. No. 7.69e+04
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 7.69e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
model4 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition', data = data).fit()
print(model4.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.910
Model: OLS Adj. R-squared: 0.908
Method: Least Squares F-statistic: 418.8
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:37:25 Log-Likelihood: -23479.
No. Observations: 2073 AIC: 4.706e+04
Df Residuals: 2023 BIC: 4.734e+04
Df Model: 49
Covariance Type: nonrobust
=============================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------------
Intercept 3.608e+04 1.25e+04 2.879 0.004 1.15e+04 6.07e+04
Zone_Class[T.FV] 3.138e+04 6872.745 4.566 0.000 1.79e+04 4.49e+04
Zone_Class[T.RL] 3.041e+04 6359.595 4.782 0.000 1.79e+04 4.29e+04
Zone_Class[T.RM] 2.459e+04 6167.933 3.987 0.000 1.25e+04 3.67e+04
LandContour[T.HLS] 1.097e+04 2992.662 3.665 0.000 5099.098 1.68e+04
LandContour[T.Lvl] 6738.3304 2286.037 2.948 0.003 2255.097 1.12e+04
Neighborhood[T.Edwards] -8739.9865 1907.686 -4.581 0.000 -1.25e+04 -4998.752
Neighborhood[T.IDOTRR] -1.644e+04 3318.202 -4.955 0.000 -2.29e+04 -9935.001
Neighborhood[T.NAmes] -6236.3190 1645.680 -3.790 0.000 -9463.724 -3008.914
Neighborhood[T.NridgHt] 2.868e+04 2125.698 13.491 0.000 2.45e+04 3.28e+04
Neighborhood[T.Somerst] 1.524e+04 1827.572 8.340 0.000 1.17e+04 1.88e+04
Neighborhood[T.StoneBr] 2.587e+04 3326.889 7.776 0.000 1.93e+04 3.24e+04
Condition1[T.Norm] 1.003e+04 1628.930 6.157 0.000 6834.137 1.32e+04
Condition1[T.PosN] 1.379e+04 3412.958 4.040 0.000 7096.664 2.05e+04
Condition2[T.PosN] -3.823e+04 1.23e+04 -3.114 0.002 -6.23e+04 -1.42e+04
Dwelling_Type[T.TwnhsE] 1.015e+04 2386.389 4.252 0.000 5465.976 1.48e+04
HouseStyle[T.2Story] -3751.9502 1558.980 -2.407 0.016 -6809.324 -694.576
Exterior2nd[T.VinylSd] 5185.6189 1164.479 4.453 0.000 2901.916 7469.321
ExterQual[T.TA] -7532.9154 1540.588 -4.890 0.000 -1.06e+04 -4511.610
BsmtQual[T.Gd] -1.504e+04 2126.608 -7.071 0.000 -1.92e+04 -1.09e+04
BsmtQual[T.No Basement] -1.272e+04 3726.564 -3.412 0.001 -2e+04 -5407.513
BsmtQual[T.TA] -1.746e+04 2516.623 -6.940 0.000 -2.24e+04 -1.25e+04
BsmtCond[T.No basement] -1.372e+04 3315.858 -4.137 0.000 -2.02e+04 -7214.399
BsmtCond[T.TA] -5672.8162 2129.787 -2.664 0.008 -9849.621 -1496.011
BsmtExposure[T.Gd] 9599.2630 1908.884 5.029 0.000 5855.678 1.33e+04
BsmtExposure[T.No] -4835.7352 1138.506 -4.247 0.000 -7068.502 -2602.968
BsmtFinType1[T.GLQ] 8695.6846 1307.894 6.649 0.000 6130.724 1.13e+04
KitchenQual[T.Fa] -3.011e+04 3948.766 -7.625 0.000 -3.79e+04 -2.24e+04
KitchenQual[T.Gd] -1.896e+04 2198.724 -8.622 0.000 -2.33e+04 -1.46e+04
KitchenQual[T.TA] -2.718e+04 2479.992 -10.960 0.000 -3.2e+04 -2.23e+04
Functional[T.Typ] 1.757e+04 1947.066 9.026 0.000 1.38e+04 2.14e+04
GarageType[T.Detchd] -5865.3577 1346.444 -4.356 0.000 -8505.920 -3224.795
GarageFinish[T.No Garage] 1.459e+04 3655.976 3.990 0.000 7419.002 2.18e+04
GarageFinish[T.RFn] -3329.4611 1113.804 -2.989 0.003 -5513.783 -1145.139
GarageQual[T.No Garage] -2.626e+04 5620.039 -4.673 0.000 -3.73e+04 -1.52e+04
GarageQual[T.TA] -1.723e+04 4979.857 -3.460 0.001 -2.7e+04 -7462.170
SaleType[T.New] 2.512e+04 6052.653 4.150 0.000 1.32e+04 3.7e+04
SaleCondition[T.Normal] 1.015e+04 1637.134 6.200 0.000 6939.803 1.34e+04
SaleCondition[T.Partial] -1.237e+04 6202.942 -1.994 0.046 -2.45e+04 -202.534
Dwell_Type -112.2275 20.477 -5.481 0.000 -152.385 -72.070
OverallQual 1543.6254 495.948 3.112 0.002 571.004 2516.247
OverallCond 3317.3383 821.257 4.039 0.000 1706.740 4927.937
TotalBsmtSF 18.7583 2.037 9.207 0.000 14.763 22.754
GrLivArea 53.0167 1.738 30.511 0.000 49.609 56.424
BsmtFullBath -2786.5927 543.881 -5.124 0.000 -3853.217 -1719.968
HalfBath 3661.2747 1301.133 2.814 0.005 1109.573 6212.976
KitchenAbvGr -1.617e+04 3252.289 -4.971 0.000 -2.25e+04 -9789.586
Fireplaces 6655.6356 892.568 7.457 0.000 4905.187 8406.084
GarageCars 4889.6647 1561.854 3.131 0.002 1826.654 7952.675
GarageArea 26.1894 5.217 5.020 0.000 15.958 36.421
==============================================================================
Omnibus: 305.507 Durbin-Watson: 2.029
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2395.761
Skew: -0.448 Prob(JB): 0.00
Kurtosis: 8.190 Cond. No. 6.54e+04
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.54e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LotFrontage 2073 non-null float64 3 LotArea 2073 non-null float64 4 Road_Type 2073 non-null object 5 Alley 2073 non-null object 6 Property_Shape 2073 non-null object 7 LandContour 2073 non-null object 8 Utilities 2073 non-null object 9 LotConfig 2073 non-null object 10 LandSlope 2073 non-null object 11 Neighborhood 2073 non-null object 12 Condition1 2073 non-null object 13 Condition2 2073 non-null object 14 Dwelling_Type 2073 non-null object 15 HouseStyle 2073 non-null object 16 OverallQual 2073 non-null int64 17 OverallCond 2073 non-null int64 18 YearBuilt 2073 non-null float64 19 YearRemodAdd 2073 non-null int64 20 RoofStyle 2073 non-null object 21 RoofMatl 2073 non-null object 22 Exterior1st 2073 non-null object 23 Exterior2nd 2073 non-null object 24 MasVnrType 2073 non-null object 25 MasVnrArea 2073 non-null float64 26 ExterQual 2073 non-null object 27 ExterCond 2073 non-null object 28 Foundation 2073 non-null object 29 BsmtQual 2073 non-null object 30 BsmtCond 2073 non-null object 31 BsmtExposure 2073 non-null object 32 BsmtFinType1 2073 non-null object 33 BsmtFinSF1 2073 non-null float64 34 BsmtFinType2 2073 non-null object 35 BsmtFinSF2 2073 non-null int64 36 BsmtUnfSF 2073 non-null float64 37 TotalBsmtSF 2073 non-null float64 38 Heating 2073 non-null object 39 HeatingQC 2073 non-null object 40 CentralAir 2073 non-null object 41 Electrical 2073 non-null object 42 FirstFlrSF 2073 non-null float64 43 SecondFlrSF 2073 non-null float64 44 LowQualFinSF 2073 non-null int64 45 GrLivArea 2073 non-null float64 46 BsmtFullBath 2073 non-null int64 47 BsmtHalfBath 2073 non-null int64 48 FullBath 2073 non-null int64 49 HalfBath 2073 non-null int64 50 BedroomAbvGr 2073 non-null int64 51 KitchenAbvGr 2073 non-null int64 52 KitchenQual 2073 non-null object 53 TotRmsAbvGrd 2073 non-null float64 54 Functional 2073 non-null object 55 Fireplaces 2073 non-null int64 56 FireplaceQu 2073 non-null object 57 GarageType 2073 non-null object 58 GarageYrBlt 2073 non-null float64 59 GarageFinish 2073 non-null object 60 GarageCars 2073 non-null int64 61 GarageArea 2073 non-null float64 62 GarageQual 2073 non-null object 63 GarageCond 2073 non-null object 64 PavedDrive 2073 non-null object 65 WoodDeckSF 2073 non-null float64 66 OpenPorchSF 2073 non-null float64 67 EnclosedPorch 2073 non-null int64 68 ThrSsnPorch 2073 non-null int64 69 ScreenPorch 2073 non-null int64 70 PoolArea 2073 non-null int64 71 PoolQC 2073 non-null object 72 Fence 2073 non-null object 73 MiscFeature 2073 non-null object 74 MiscVal 2073 non-null int64 75 MoSold 2073 non-null int64 76 YrSold 2073 non-null int64 77 SaleType 2073 non-null object 78 SaleCondition 2073 non-null object 79 Property_Sale_Price 2073 non-null float64 dtypes: float64(16), int64(21), object(43) memory usage: 1.3+ MB
data1 = data[['Dwell_Type', 'Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2', 'Dwelling_Type',
'HouseStyle', 'OverallQual', 'OverallCond', 'Exterior2nd', 'ExterQual', 'BsmtQual', 'BsmtCond',
'BsmtExposure', 'BsmtFinType1', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
'KitchenQual', 'Functional', 'Fireplaces', 'GarageType', 'GarageFinish', 'GarageCars', 'GarageArea',
'GarageQual', 'SaleType', 'SaleCondition', 'Property_Sale_Price']]
data1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LandContour 2073 non-null object 3 Neighborhood 2073 non-null object 4 Condition1 2073 non-null object 5 Condition2 2073 non-null object 6 Dwelling_Type 2073 non-null object 7 HouseStyle 2073 non-null object 8 OverallQual 2073 non-null int64 9 OverallCond 2073 non-null int64 10 Exterior2nd 2073 non-null object 11 ExterQual 2073 non-null object 12 BsmtQual 2073 non-null object 13 BsmtCond 2073 non-null object 14 BsmtExposure 2073 non-null object 15 BsmtFinType1 2073 non-null object 16 TotalBsmtSF 2073 non-null float64 17 GrLivArea 2073 non-null float64 18 BsmtFullBath 2073 non-null int64 19 HalfBath 2073 non-null int64 20 KitchenAbvGr 2073 non-null int64 21 KitchenQual 2073 non-null object 22 Functional 2073 non-null object 23 Fireplaces 2073 non-null int64 24 GarageType 2073 non-null object 25 GarageFinish 2073 non-null object 26 GarageCars 2073 non-null int64 27 GarageArea 2073 non-null float64 28 GarageQual 2073 non-null object 29 SaleType 2073 non-null object 30 SaleCondition 2073 non-null object 31 Property_Sale_Price 2073 non-null float64 dtypes: float64(4), int64(8), object(20) memory usage: 518.4+ KB
# these are predicted values, called y hat also
predict = model4.predict()
predict.shape
(2073,)
# Value Count Of Target Variable
data1.Property_Sale_Price.value_counts().sum()
2073
# Residuals = Actual(y) - Predictor(y hat)
residuals = data1.Property_Sale_Price - predict
residuals
0 6090.577315
1 -10542.106317
2 2798.039754
3 -60127.286247
4 -37848.254893
...
2068 -11960.633495
2069 18238.107138
2070 -14212.363893
2071 -37269.519371
2072 -39547.423624
Name: Property_Sale_Price, Length: 2073, dtype: float64
# Now We have a new column in our data
data1['predict'] = predict
# Now We have an another new column in our data
data1['residuals'] = residuals
# Now We Create Observation Number
obs = np.arange(2073)
obs
obsno = pd.DataFrame(obs) # convert array into a data frame
data1['obsno'] = obsno # craete a new variable/column 'obsno' in yr data
data1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LandContour 2073 non-null object 3 Neighborhood 2073 non-null object 4 Condition1 2073 non-null object 5 Condition2 2073 non-null object 6 Dwelling_Type 2073 non-null object 7 HouseStyle 2073 non-null object 8 OverallQual 2073 non-null int64 9 OverallCond 2073 non-null int64 10 Exterior2nd 2073 non-null object 11 ExterQual 2073 non-null object 12 BsmtQual 2073 non-null object 13 BsmtCond 2073 non-null object 14 BsmtExposure 2073 non-null object 15 BsmtFinType1 2073 non-null object 16 TotalBsmtSF 2073 non-null float64 17 GrLivArea 2073 non-null float64 18 BsmtFullBath 2073 non-null int64 19 HalfBath 2073 non-null int64 20 KitchenAbvGr 2073 non-null int64 21 KitchenQual 2073 non-null object 22 Functional 2073 non-null object 23 Fireplaces 2073 non-null int64 24 GarageType 2073 non-null object 25 GarageFinish 2073 non-null object 26 GarageCars 2073 non-null int64 27 GarageArea 2073 non-null float64 28 GarageQual 2073 non-null object 29 SaleType 2073 non-null object 30 SaleCondition 2073 non-null object 31 Property_Sale_Price 2073 non-null float64 32 predict 2073 non-null float64 33 residuals 2073 non-null float64 34 obsno 2073 non-null int32 dtypes: float64(6), int32(1), int64(8), object(20) memory usage: 558.9+ KB
make histogram of residuals
And check whether its approximately bell-shaped, symmetrical
plt.figure(figsize=(12,10))
sns.distplot(data1.residuals, color='skyblue')
plt.title('Histogram of Residuals')
plt.ylabel('Prob')
plt.xlabel('Residuals')
plt.show()
import math
data1['residuals'] = np.sqrt(data1.residuals)
plt.figure(figsize=(12,10))
sns.distplot(data1.residuals, color='skyblue')
plt.title('Histogram of Residuals')
plt.ylabel('Prob')
plt.xlabel('Residuals')
plt.show()
Make scatter plots,
x = each continuous predictor one by one
y = response variable
see whether a linear relationship is visible?
data1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LandContour 2073 non-null object 3 Neighborhood 2073 non-null object 4 Condition1 2073 non-null object 5 Condition2 2073 non-null object 6 Dwelling_Type 2073 non-null object 7 HouseStyle 2073 non-null object 8 OverallQual 2073 non-null int64 9 OverallCond 2073 non-null int64 10 Exterior2nd 2073 non-null object 11 ExterQual 2073 non-null object 12 BsmtQual 2073 non-null object 13 BsmtCond 2073 non-null object 14 BsmtExposure 2073 non-null object 15 BsmtFinType1 2073 non-null object 16 TotalBsmtSF 2073 non-null float64 17 GrLivArea 2073 non-null float64 18 BsmtFullBath 2073 non-null int64 19 HalfBath 2073 non-null int64 20 KitchenAbvGr 2073 non-null int64 21 KitchenQual 2073 non-null object 22 Functional 2073 non-null object 23 Fireplaces 2073 non-null int64 24 GarageType 2073 non-null object 25 GarageFinish 2073 non-null object 26 GarageCars 2073 non-null int64 27 GarageArea 2073 non-null float64 28 GarageQual 2073 non-null object 29 SaleType 2073 non-null object 30 SaleCondition 2073 non-null object 31 Property_Sale_Price 2073 non-null float64 32 predict 2073 non-null float64 33 residuals 1057 non-null float64 34 obsno 2073 non-null int32 dtypes: float64(6), int32(1), int64(8), object(20) memory usage: 558.9+ KB
numerical_data1 = data1.select_dtypes(include=np.number)
print(len(numerical_data1.columns))
numerical_data1.columns
15
Index(['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea',
'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 'Fireplaces', 'GarageCars',
'GarageArea', 'Property_Sale_Price', 'predict', 'residuals', 'obsno'],
dtype='object')
categorical_data1 = data1.select_dtypes(include = 'O')
print(len(categorical_data1.columns))
categorical_data1.columns
20
Index(['Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2',
'Dwelling_Type', 'HouseStyle', 'Exterior2nd', 'ExterQual', 'BsmtQual',
'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'KitchenQual', 'Functional',
'GarageType', 'GarageFinish', 'GarageQual', 'SaleType',
'SaleCondition'],
dtype='object')
### With Good Predictors
a = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
'Fireplaces', 'GarageCars', 'GarageArea', 'Property_Sale_Price', 'predict', 'residuals', 'obsno']
data1_pp = data1[a]
sns.pairplot(data1_pp ,palette = sns.color_palette('Set3_r'), markers = ['>'], plot_kws = {'facecolors':'#00FF00'})
<seaborn.axisgrid.PairGrid at 0x29b834777c0>
# visualize the relationship between the features and the response using scatterplots
sns.pairplot(data1, x_vars = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
'Fireplaces', 'GarageCars', 'GarageArea', 'predict', 'residuals', 'obsno'],
y_vars = 'Property_Sale_Price', size=7, aspect=0.7)
<seaborn.axisgrid.PairGrid at 0x29b8c57bdf0>
Make scatter plot,
x = obsno
y = residuals
if, no geometric pattern is visible, assumption of INDEPENDENCE OF ERROR is holding good and not violated
plt.figure(figsize=(12,10))
sns.scatterplot(x = data1.obsno, y = data1.residuals, palette='spring_r', color='darkslateblue', markers='+')
plt.title('Scatterplot of obsno vs residuals')
Text(0.5, 1.0, 'Scatterplot of obsno vs residuals')
Make scatter plot,
x = predict
y = residuals
if, no geometric pattern is visible, assumption of homoscadasticity is holding good and not violated
plt.figure(figsize=(12,10))
sns.scatterplot(x = data1.predict, y = data1.residuals, palette='spring', color='teal')
plt.title('Scatterplot of predict vs residuals')
Text(0.5, 1.0, 'Scatterplot of predict vs residuals')
data1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2073 entries, 0 to 2072 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 2073 non-null int64 1 Zone_Class 2073 non-null object 2 LandContour 2073 non-null object 3 Neighborhood 2073 non-null object 4 Condition1 2073 non-null object 5 Condition2 2073 non-null object 6 Dwelling_Type 2073 non-null object 7 HouseStyle 2073 non-null object 8 OverallQual 2073 non-null int64 9 OverallCond 2073 non-null int64 10 Exterior2nd 2073 non-null object 11 ExterQual 2073 non-null object 12 BsmtQual 2073 non-null object 13 BsmtCond 2073 non-null object 14 BsmtExposure 2073 non-null object 15 BsmtFinType1 2073 non-null object 16 TotalBsmtSF 2073 non-null float64 17 GrLivArea 2073 non-null float64 18 BsmtFullBath 2073 non-null int64 19 HalfBath 2073 non-null int64 20 KitchenAbvGr 2073 non-null int64 21 KitchenQual 2073 non-null object 22 Functional 2073 non-null object 23 Fireplaces 2073 non-null int64 24 GarageType 2073 non-null object 25 GarageFinish 2073 non-null object 26 GarageCars 2073 non-null int64 27 GarageArea 2073 non-null float64 28 GarageQual 2073 non-null object 29 SaleType 2073 non-null object 30 SaleCondition 2073 non-null object 31 Property_Sale_Price 2073 non-null float64 32 predict 2073 non-null float64 33 residuals 1057 non-null float64 34 obsno 2073 non-null int32 dtypes: float64(6), int32(1), int64(8), object(20) memory usage: 558.9+ KB
x = data1[['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath',
'KitchenAbvGr', 'Fireplaces', 'GarageCars', 'GarageArea']]
x.columns
Index(['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea',
'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 'Fireplaces', 'GarageCars',
'GarageArea'],
dtype='object')
vif_data = pd.DataFrame()
vif_data["feature"] = x.columns
# calculating VIF for each feature
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif_data["VIF"] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]
print(vif_data)
feature VIF 0 Dwell_Type 4.087129 1 OverallQual 35.242275 2 OverallCond 35.294751 3 TotalBsmtSF 13.048126 4 GrLivArea 24.222567 5 BsmtFullBath 6.411239 6 HalfBath 2.436959 7 KitchenAbvGr 23.739868 8 Fireplaces 2.695333 9 GarageCars 36.031353 10 GarageArea 32.552027
# import numpy and statsmodels for durbin watson
import numpy as np
from statsmodels.stats.stattools import durbin_watson
gfg = durbin_watson(model4.resid)
print("Durbin Watson Value :", round(gfg, 3))
Durbin Watson Value : 2.029
model5 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFullBath + HalfBath + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageQual + SaleType + SaleCondition', data = data1).fit()
print(model5.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.810
Model: OLS Adj. R-squared: 0.806
Method: Least Squares F-statistic: 205.6
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:38:49 Log-Likelihood: -24258.
No. Observations: 2073 AIC: 4.860e+04
Df Residuals: 2030 BIC: 4.884e+04
Df Model: 42
Covariance Type: nonrobust
=============================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------------
Intercept 2.339e+05 1.41e+04 16.613 0.000 2.06e+05 2.62e+05
Zone_Class[T.FV] 1.119e+04 9919.177 1.129 0.259 -8258.353 3.06e+04
Zone_Class[T.RL] 2.25e+04 9196.804 2.447 0.014 4467.158 4.05e+04
Zone_Class[T.RM] 1.513e+04 8909.359 1.698 0.090 -2341.613 3.26e+04
LandContour[T.HLS] 6554.2324 4301.927 1.524 0.128 -1882.420 1.5e+04
LandContour[T.Lvl] 1801.2449 3283.982 0.548 0.583 -4639.082 8241.571
Neighborhood[T.Edwards] -1.32e+04 2746.788 -4.804 0.000 -1.86e+04 -7808.354
Neighborhood[T.IDOTRR] -3.606e+04 4704.742 -7.665 0.000 -4.53e+04 -2.68e+04
Neighborhood[T.NAmes] -9655.5830 2380.961 -4.055 0.000 -1.43e+04 -4986.201
Neighborhood[T.NridgHt] 4.941e+04 2997.896 16.482 0.000 4.35e+04 5.53e+04
Neighborhood[T.Somerst] 2.141e+04 2638.714 8.115 0.000 1.62e+04 2.66e+04
Neighborhood[T.StoneBr] 3.796e+04 4803.678 7.902 0.000 2.85e+04 4.74e+04
Condition1[T.Norm] 3393.9040 2348.683 1.445 0.149 -1212.175 7999.983
Condition1[T.PosN] 2.12e+04 4933.293 4.297 0.000 1.15e+04 3.09e+04
Condition2[T.PosN] -1788.1958 1.77e+04 -0.101 0.919 -3.65e+04 3.29e+04
Dwelling_Type[T.TwnhsE] 6071.4912 2555.261 2.376 0.018 1060.284 1.11e+04
HouseStyle[T.2Story] 1.336e+04 1887.485 7.080 0.000 9660.955 1.71e+04
Exterior2nd[T.VinylSd] 6744.2790 1680.232 4.014 0.000 3449.120 1e+04
ExterQual[T.TA] -1.589e+04 2195.229 -7.237 0.000 -2.02e+04 -1.16e+04
BsmtQual[T.Gd] -3.083e+04 3041.206 -10.138 0.000 -3.68e+04 -2.49e+04
BsmtQual[T.No Basement] -4.374e+04 5143.894 -8.504 0.000 -5.38e+04 -3.37e+04
BsmtQual[T.TA] -3.617e+04 3553.895 -10.177 0.000 -4.31e+04 -2.92e+04
BsmtCond[T.No basement] -1.705e+04 4708.665 -3.621 0.000 -2.63e+04 -7814.258
BsmtCond[T.TA] -7894.9289 3090.275 -2.555 0.011 -1.4e+04 -1834.488
BsmtExposure[T.Gd] 1.676e+04 2744.060 6.109 0.000 1.14e+04 2.21e+04
BsmtExposure[T.No] -3952.1219 1641.972 -2.407 0.016 -7172.247 -731.996
BsmtFinType1[T.GLQ] 1.026e+04 1893.772 5.420 0.000 6550.207 1.4e+04
KitchenQual[T.Fa] -5.267e+04 5686.344 -9.262 0.000 -6.38e+04 -4.15e+04
KitchenQual[T.Gd] -2.699e+04 3171.736 -8.508 0.000 -3.32e+04 -2.08e+04
KitchenQual[T.TA] -4.336e+04 3554.448 -12.200 0.000 -5.03e+04 -3.64e+04
Functional[T.Typ] 4239.5977 2752.587 1.540 0.124 -1158.592 9637.788
GarageType[T.Detchd] -9396.7787 1929.507 -4.870 0.000 -1.32e+04 -5612.759
GarageFinish[T.No Garage] -8206.9863 4983.919 -1.647 0.100 -1.8e+04 1567.142
GarageFinish[T.RFn] -2599.7181 1605.160 -1.620 0.105 -5747.650 548.214
GarageQual[T.No Garage] -3.662e+04 8084.043 -4.530 0.000 -5.25e+04 -2.08e+04
GarageQual[T.TA] -2.504e+04 7186.063 -3.484 0.001 -3.91e+04 -1.09e+04
SaleType[T.New] 3.294e+04 8784.680 3.750 0.000 1.57e+04 5.02e+04
SaleCondition[T.Normal] 9088.0610 2370.303 3.834 0.000 4439.581 1.37e+04
SaleCondition[T.Partial] -2.026e+04 8997.612 -2.252 0.024 -3.79e+04 -2614.387
Dwell_Type -225.6453 29.135 -7.745 0.000 -282.784 -168.507
BsmtFullBath -1719.4163 776.919 -2.213 0.027 -3243.058 -195.775
HalfBath 8354.9676 1808.244 4.620 0.000 4808.760 1.19e+04
Fireplaces 1.979e+04 1210.433 16.350 0.000 1.74e+04 2.22e+04
==============================================================================
Omnibus: 114.322 Durbin-Watson: 2.008
Prob(Omnibus): 0.000 Jarque-Bera (JB): 282.668
Skew: 0.310 Prob(JB): 4.16e-62
Kurtosis: 4.699 Cond. No. 1.45e+03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.45e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
from sklearn.metrics import mean_squared_error
MSE = mean_squared_error(data1.Property_Sale_Price ,data1.predict)
print("Mean Squared Error (MSE) :", round(MSE, 3))
Mean Squared Error (MSE) : 402937930.219
RMSE = np.sqrt(MSE)
print("Root Mean Squared Error (RMSE) :", round(RMSE, 3))
Root Mean Squared Error (RMSE) : 20073.314
# Split DAta into train and test in approx 80:20 ratio
trn = data.iloc[0 : 1660, ]
trn.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1660 entries, 0 to 1659 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 1660 non-null int64 1 Zone_Class 1660 non-null object 2 LotFrontage 1660 non-null float64 3 LotArea 1660 non-null float64 4 Road_Type 1660 non-null object 5 Alley 1660 non-null object 6 Property_Shape 1660 non-null object 7 LandContour 1660 non-null object 8 Utilities 1660 non-null object 9 LotConfig 1660 non-null object 10 LandSlope 1660 non-null object 11 Neighborhood 1660 non-null object 12 Condition1 1660 non-null object 13 Condition2 1660 non-null object 14 Dwelling_Type 1660 non-null object 15 HouseStyle 1660 non-null object 16 OverallQual 1660 non-null int64 17 OverallCond 1660 non-null int64 18 YearBuilt 1660 non-null float64 19 YearRemodAdd 1660 non-null int64 20 RoofStyle 1660 non-null object 21 RoofMatl 1660 non-null object 22 Exterior1st 1660 non-null object 23 Exterior2nd 1660 non-null object 24 MasVnrType 1660 non-null object 25 MasVnrArea 1660 non-null float64 26 ExterQual 1660 non-null object 27 ExterCond 1660 non-null object 28 Foundation 1660 non-null object 29 BsmtQual 1660 non-null object 30 BsmtCond 1660 non-null object 31 BsmtExposure 1660 non-null object 32 BsmtFinType1 1660 non-null object 33 BsmtFinSF1 1660 non-null float64 34 BsmtFinType2 1660 non-null object 35 BsmtFinSF2 1660 non-null int64 36 BsmtUnfSF 1660 non-null float64 37 TotalBsmtSF 1660 non-null float64 38 Heating 1660 non-null object 39 HeatingQC 1660 non-null object 40 CentralAir 1660 non-null object 41 Electrical 1660 non-null object 42 FirstFlrSF 1660 non-null float64 43 SecondFlrSF 1660 non-null float64 44 LowQualFinSF 1660 non-null int64 45 GrLivArea 1660 non-null float64 46 BsmtFullBath 1660 non-null int64 47 BsmtHalfBath 1660 non-null int64 48 FullBath 1660 non-null int64 49 HalfBath 1660 non-null int64 50 BedroomAbvGr 1660 non-null int64 51 KitchenAbvGr 1660 non-null int64 52 KitchenQual 1660 non-null object 53 TotRmsAbvGrd 1660 non-null float64 54 Functional 1660 non-null object 55 Fireplaces 1660 non-null int64 56 FireplaceQu 1660 non-null object 57 GarageType 1660 non-null object 58 GarageYrBlt 1660 non-null float64 59 GarageFinish 1660 non-null object 60 GarageCars 1660 non-null int64 61 GarageArea 1660 non-null float64 62 GarageQual 1660 non-null object 63 GarageCond 1660 non-null object 64 PavedDrive 1660 non-null object 65 WoodDeckSF 1660 non-null float64 66 OpenPorchSF 1660 non-null float64 67 EnclosedPorch 1660 non-null int64 68 ThrSsnPorch 1660 non-null int64 69 ScreenPorch 1660 non-null int64 70 PoolArea 1660 non-null int64 71 PoolQC 1660 non-null object 72 Fence 1660 non-null object 73 MiscFeature 1660 non-null object 74 MiscVal 1660 non-null int64 75 MoSold 1660 non-null int64 76 YrSold 1660 non-null int64 77 SaleType 1660 non-null object 78 SaleCondition 1660 non-null object 79 Property_Sale_Price 1660 non-null float64 dtypes: float64(16), int64(21), object(43) memory usage: 1.0+ MB
test = data.iloc[1660: , ]
test.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 413 entries, 1660 to 2072 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 413 non-null int64 1 Zone_Class 413 non-null object 2 LotFrontage 413 non-null float64 3 LotArea 413 non-null float64 4 Road_Type 413 non-null object 5 Alley 413 non-null object 6 Property_Shape 413 non-null object 7 LandContour 413 non-null object 8 Utilities 413 non-null object 9 LotConfig 413 non-null object 10 LandSlope 413 non-null object 11 Neighborhood 413 non-null object 12 Condition1 413 non-null object 13 Condition2 413 non-null object 14 Dwelling_Type 413 non-null object 15 HouseStyle 413 non-null object 16 OverallQual 413 non-null int64 17 OverallCond 413 non-null int64 18 YearBuilt 413 non-null float64 19 YearRemodAdd 413 non-null int64 20 RoofStyle 413 non-null object 21 RoofMatl 413 non-null object 22 Exterior1st 413 non-null object 23 Exterior2nd 413 non-null object 24 MasVnrType 413 non-null object 25 MasVnrArea 413 non-null float64 26 ExterQual 413 non-null object 27 ExterCond 413 non-null object 28 Foundation 413 non-null object 29 BsmtQual 413 non-null object 30 BsmtCond 413 non-null object 31 BsmtExposure 413 non-null object 32 BsmtFinType1 413 non-null object 33 BsmtFinSF1 413 non-null float64 34 BsmtFinType2 413 non-null object 35 BsmtFinSF2 413 non-null int64 36 BsmtUnfSF 413 non-null float64 37 TotalBsmtSF 413 non-null float64 38 Heating 413 non-null object 39 HeatingQC 413 non-null object 40 CentralAir 413 non-null object 41 Electrical 413 non-null object 42 FirstFlrSF 413 non-null float64 43 SecondFlrSF 413 non-null float64 44 LowQualFinSF 413 non-null int64 45 GrLivArea 413 non-null float64 46 BsmtFullBath 413 non-null int64 47 BsmtHalfBath 413 non-null int64 48 FullBath 413 non-null int64 49 HalfBath 413 non-null int64 50 BedroomAbvGr 413 non-null int64 51 KitchenAbvGr 413 non-null int64 52 KitchenQual 413 non-null object 53 TotRmsAbvGrd 413 non-null float64 54 Functional 413 non-null object 55 Fireplaces 413 non-null int64 56 FireplaceQu 413 non-null object 57 GarageType 413 non-null object 58 GarageYrBlt 413 non-null float64 59 GarageFinish 413 non-null object 60 GarageCars 413 non-null int64 61 GarageArea 413 non-null float64 62 GarageQual 413 non-null object 63 GarageCond 413 non-null object 64 PavedDrive 413 non-null object 65 WoodDeckSF 413 non-null float64 66 OpenPorchSF 413 non-null float64 67 EnclosedPorch 413 non-null int64 68 ThrSsnPorch 413 non-null int64 69 ScreenPorch 413 non-null int64 70 PoolArea 413 non-null int64 71 PoolQC 413 non-null object 72 Fence 413 non-null object 73 MiscFeature 413 non-null object 74 MiscVal 413 non-null int64 75 MoSold 413 non-null int64 76 YrSold 413 non-null int64 77 SaleType 413 non-null object 78 SaleCondition 413 non-null object 79 Property_Sale_Price 413 non-null float64 dtypes: float64(16), int64(21), object(43) memory usage: 258.3+ KB
model_1 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LotFrontage + LotArea + Road_Type + Alley + Property_Shape + LandContour + Utilities + LotConfig + LandSlope + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + YearBuilt + YearRemodAdd + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + MasVnrArea + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinSF1 + BsmtFinType2 + BsmtFinSF2 + BsmtUnfSF + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + SecondFlrSF + LowQualFinSF + GrLivArea + BsmtFullBath + BsmtHalfBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + TotRmsAbvGrd + Functional + Fireplaces + FireplaceQu + GarageType + GarageYrBlt + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + WoodDeckSF + OpenPorchSF + EnclosedPorch + ThrSsnPorch + ScreenPorch + PoolArea + PoolQC + Fence + MiscFeature + MiscVal + MoSold + YrSold + SaleType + SaleCondition', data = trn).fit()
print(model_1.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.922
Model: OLS Adj. R-squared: 0.916
Method: Least Squares F-statistic: 169.5
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:38:53 Log-Likelihood: -18690.
No. Observations: 1660 AIC: 3.760e+04
Df Residuals: 1551 BIC: 3.819e+04
Df Model: 108
Covariance Type: nonrobust
===============================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------------
Intercept -6.749e+05 7.95e+05 -0.849 0.396 -2.23e+06 8.84e+05
Zone_Class[T.FV] 2.963e+04 7727.545 3.835 0.000 1.45e+04 4.48e+04
Zone_Class[T.RL] 2.755e+04 7116.147 3.872 0.000 1.36e+04 4.15e+04
Zone_Class[T.RM] 2.454e+04 6921.022 3.545 0.000 1.1e+04 3.81e+04
Road_Type[T.Pave] 1.968e+04 8051.279 2.445 0.015 3890.304 3.55e+04
Alley[T.No alley access] 6485.3589 2862.152 2.266 0.024 871.263 1.21e+04
Property_Shape[T.Reg] -889.2642 1153.934 -0.771 0.441 -3152.699 1374.171
LandContour[T.HLS] 4827.0355 3356.096 1.438 0.151 -1755.929 1.14e+04
LandContour[T.Lvl] 2637.4755 2592.292 1.017 0.309 -2447.291 7722.242
Utilities[T.NoSeWa] -4.043e+04 2.04e+04 -1.984 0.047 -8.04e+04 -466.047
LotConfig[T.Inside] -1295.0973 1156.392 -1.120 0.263 -3563.354 973.159
LandSlope[T.Mod] 5929.2495 2872.915 2.064 0.039 294.042 1.16e+04
LandSlope[T.Sev] -3948.6265 6070.068 -0.651 0.515 -1.59e+04 7957.780
Neighborhood[T.Edwards] -8897.2469 2231.996 -3.986 0.000 -1.33e+04 -4519.199
Neighborhood[T.IDOTRR] -1.244e+04 3747.368 -3.321 0.001 -1.98e+04 -5094.071
Neighborhood[T.NAmes] -6566.5026 1900.185 -3.456 0.001 -1.03e+04 -2839.300
Neighborhood[T.NridgHt] 2.544e+04 2412.632 10.544 0.000 2.07e+04 3.02e+04
Neighborhood[T.Somerst] 1.345e+04 2091.908 6.427 0.000 9342.206 1.75e+04
Neighborhood[T.StoneBr] 2.371e+04 3666.257 6.468 0.000 1.65e+04 3.09e+04
Condition1[T.Norm] 9301.3870 1790.766 5.194 0.000 5788.810 1.28e+04
Condition1[T.PosN] 9189.5809 4006.355 2.294 0.022 1331.138 1.7e+04
Condition2[T.PosN] -3.775e+04 1.24e+04 -3.035 0.002 -6.22e+04 -1.34e+04
Dwelling_Type[T.TwnhsE] 7897.6068 2735.272 2.887 0.004 2532.386 1.33e+04
HouseStyle[T.2Story] -4543.1375 2076.776 -2.188 0.029 -8616.722 -469.553
RoofStyle[T.Hip] 456.4936 1328.581 0.344 0.731 -2149.510 3062.497
RoofMatl[T.WdShngl] 1.471e+04 5977.673 2.461 0.014 2984.379 2.64e+04
Exterior1st[T.MetalSd] -2931.8808 4284.591 -0.684 0.494 -1.13e+04 5472.322
Exterior1st[T.Plywood] 1461.1274 4595.671 0.318 0.751 -7553.257 1.05e+04
Exterior1st[T.VinylSd] -5954.0446 5124.057 -1.162 0.245 -1.6e+04 4096.767
Exterior2nd[T.VinylSd] 8008.9386 2788.594 2.872 0.004 2539.126 1.35e+04
MasVnrType[T.None] -562.5398 1822.101 -0.309 0.758 -4136.581 3011.501
MasVnrType[T.Stone] 3348.7692 2112.328 1.585 0.113 -794.552 7492.090
ExterQual[T.TA] -7596.6591 1748.176 -4.345 0.000 -1.1e+04 -4167.621
ExterCond[T.TA] -3203.3515 3884.308 -0.825 0.410 -1.08e+04 4415.699
Foundation[T.CBlock] 433.6483 2155.065 0.201 0.841 -3793.501 4660.797
Foundation[T.PConc] 2032.2332 2400.371 0.847 0.397 -2676.082 6740.549
BsmtQual[T.Gd] -1.197e+04 2439.586 -4.908 0.000 -1.68e+04 -7187.693
BsmtQual[T.No Basement] -1.003e+04 4467.750 -2.246 0.025 -1.88e+04 -1270.274
BsmtQual[T.TA] -1.092e+04 2942.495 -3.712 0.000 -1.67e+04 -5151.988
BsmtCond[T.No basement] -1.44e+04 3908.856 -3.685 0.000 -2.21e+04 -6735.141
BsmtCond[T.TA] -3044.4932 2386.896 -1.276 0.202 -7726.377 1637.391
BsmtExposure[T.Gd] 7795.1322 2145.021 3.634 0.000 3587.685 1.2e+04
BsmtExposure[T.No] -2906.7901 1299.539 -2.237 0.025 -5455.829 -357.751
BsmtFinType1[T.GLQ] 6687.7085 1594.395 4.195 0.000 3560.311 9815.106
BsmtFinType2[T.Rec] -2.467e+04 6214.959 -3.969 0.000 -3.69e+04 -1.25e+04
BsmtFinType2[T.Unf] -2.113e+04 5879.874 -3.593 0.000 -3.27e+04 -9592.136
Heating[T.Grav] -5664.1501 5295.845 -1.070 0.285 -1.61e+04 4723.622
HeatingQC[T.TA] -1724.7267 1312.946 -1.314 0.189 -4300.063 850.609
CentralAir[T.Y] 4793.3559 2622.360 1.828 0.068 -350.390 9937.102
Electrical[T.SBrkr] -3546.3578 2024.558 -1.752 0.080 -7517.518 424.803
KitchenQual[T.Fa] -2.001e+04 4418.394 -4.528 0.000 -2.87e+04 -1.13e+04
KitchenQual[T.Gd] -1.742e+04 2473.706 -7.042 0.000 -2.23e+04 -1.26e+04
KitchenQual[T.TA] -2.194e+04 2832.384 -7.747 0.000 -2.75e+04 -1.64e+04
Functional[T.Typ] 1.816e+04 2195.582 8.273 0.000 1.39e+04 2.25e+04
FireplaceQu[T.Fa] -5996.3476 5376.885 -1.115 0.265 -1.65e+04 4550.383
FireplaceQu[T.Gd] 239.5142 4157.933 0.058 0.954 -7916.250 8395.279
FireplaceQu[T.No Fireplace] 755.0856 4727.501 0.160 0.873 -8517.882 1e+04
FireplaceQu[T.TA] -336.1986 4289.009 -0.078 0.938 -8749.067 8076.669
GarageType[T.Detchd] -937.6517 1545.568 -0.607 0.544 -3969.275 2093.972
GarageFinish[T.No Garage] 2.286e+04 4857.703 4.706 0.000 1.33e+04 3.24e+04
GarageFinish[T.RFn] -2779.9769 1242.658 -2.237 0.025 -5217.443 -342.510
GarageQual[T.No Garage] -2.363e+04 6344.235 -3.725 0.000 -3.61e+04 -1.12e+04
GarageQual[T.TA] -1.541e+04 5325.951 -2.894 0.004 -2.59e+04 -4964.888
GarageCond[T.TA] 4020.9634 3752.310 1.072 0.284 -3339.172 1.14e+04
PavedDrive[T.Y] 1854.0195 2128.706 0.871 0.384 -2321.426 6029.465
PoolQC[T.Fa] -1.888e+04 2.16e+04 -0.873 0.383 -6.13e+04 2.35e+04
PoolQC[T.Gd] -5.973e+04 2.02e+04 -2.951 0.003 -9.94e+04 -2e+04
PoolQC[T.No Pool] 3.982e+05 4.96e+04 8.032 0.000 3.01e+05 4.95e+05
Fence[T.No Fence] -89.7155 1488.254 -0.060 0.952 -3008.918 2829.487
MiscFeature[T.Shed] 4627.9797 2880.953 1.606 0.108 -1022.995 1.03e+04
SaleType[T.New] 2.263e+04 6414.540 3.527 0.000 1e+04 3.52e+04
SaleCondition[T.Normal] 9818.3825 1829.095 5.368 0.000 6230.623 1.34e+04
SaleCondition[T.Partial] -8682.4420 6614.107 -1.313 0.189 -2.17e+04 4291.093
Dwell_Type -74.4296 24.175 -3.079 0.002 -121.848 -27.011
LotFrontage 17.5421 34.166 0.513 0.608 -49.474 84.558
LotArea 0.5284 0.180 2.942 0.003 0.176 0.881
OverallQual 3437.6671 617.771 5.565 0.000 2225.912 4649.422
OverallCond 2882.9417 966.907 2.982 0.003 986.358 4779.526
YearBuilt 18.1211 35.294 0.513 0.608 -51.108 87.350
YearRemodAdd 217.8828 38.083 5.721 0.000 143.182 292.583
MasVnrArea 5.2840 6.626 0.798 0.425 -7.712 18.280
BsmtFinSF1 -34.9881 8.868 -3.946 0.000 -52.382 -17.594
BsmtFinSF2 -37.7913 9.405 -4.018 0.000 -56.239 -19.343
BsmtUnfSF -44.9115 8.998 -4.991 0.000 -62.562 -27.261
TotalBsmtSF 67.6645 9.811 6.897 0.000 48.421 86.908
FirstFlrSF -20.2990 8.392 -2.419 0.016 -36.760 -3.838
SecondFlrSF -9.4071 7.545 -1.247 0.213 -24.206 5.392
LowQualFinSF -35.7420 12.506 -2.858 0.004 -60.272 -11.212
GrLivArea 59.7318 7.970 7.495 0.000 44.099 75.365
BsmtFullBath -1285.8205 741.784 -1.733 0.083 -2740.826 169.185
BsmtHalfBath -744.4021 2273.542 -0.327 0.743 -5203.943 3715.139
FullBath 2557.2845 1611.948 1.586 0.113 -604.543 5719.112
HalfBath 1797.2025 1544.836 1.163 0.245 -1232.986 4827.391
BedroomAbvGr -242.7745 1055.018 -0.230 0.818 -2312.186 1826.637
KitchenAbvGr -1.929e+04 3769.829 -5.118 0.000 -2.67e+04 -1.19e+04
TotRmsAbvGrd 836.2509 673.344 1.242 0.214 -484.509 2157.011
Fireplaces 5986.1678 1885.715 3.174 0.002 2287.348 9684.988
GarageYrBlt -123.4127 42.658 -2.893 0.004 -207.085 -39.740
GarageCars 3962.5207 1771.991 2.236 0.025 486.769 7438.272
GarageArea 30.5763 6.126 4.992 0.000 18.561 42.592
WoodDeckSF 16.5559 4.808 3.444 0.001 7.125 25.986
OpenPorchSF 22.7239 11.116 2.044 0.041 0.919 44.529
EnclosedPorch 5.5731 9.304 0.599 0.549 -12.677 23.823
ThrSsnPorch 29.0589 17.311 1.679 0.093 -4.897 63.015
ScreenPorch 30.9026 9.514 3.248 0.001 12.241 49.564
PoolArea 699.3293 87.372 8.004 0.000 527.949 870.709
MiscVal 1.2893 1.089 1.184 0.237 -0.847 3.426
MoSold 179.2526 186.067 0.963 0.336 -185.716 544.221
YrSold 28.1100 393.102 0.072 0.943 -742.958 799.178
==============================================================================
Omnibus: 157.247 Durbin-Watson: 2.037
Prob(Omnibus): 0.000 Jarque-Bera (JB): 956.841
Skew: -0.184 Prob(JB): 1.68e-208
Kurtosis: 6.701 Cond. No. 1.90e+07
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.9e+07. This might indicate that there are
strong multicollinearity or other numerical problems.
model_2 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + Road_Type + Alley + Property_Shape + LandContour + LotConfig + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinType2 + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + GrLivArea + BsmtFullBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + Functional + Fireplaces + FireplaceQu + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + Fence + MiscFeature + SaleType + SaleCondition', data = trn).fit()
print(model2.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.915
Model: OLS Adj. R-squared: 0.911
Method: Least Squares F-statistic: 222.9
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:38:54 Log-Likelihood: -23428.
No. Observations: 2073 AIC: 4.705e+04
Df Residuals: 1977 BIC: 4.759e+04
Df Model: 95
Covariance Type: nonrobust
===============================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------------
Intercept 2.949e+04 2.73e+04 1.081 0.280 -2.4e+04 8.3e+04
Zone_Class[T.FV] 3.107e+04 7231.259 4.297 0.000 1.69e+04 4.53e+04
Zone_Class[T.RL] 3.11e+04 6714.572 4.632 0.000 1.79e+04 4.43e+04
Zone_Class[T.RM] 2.663e+04 6525.447 4.080 0.000 1.38e+04 3.94e+04
Road_Type[T.Pave] 1.35e+04 8354.476 1.616 0.106 -2880.769 2.99e+04
Alley[T.No alley access] 3874.4335 2689.289 1.441 0.150 -1399.704 9148.571
Property_Shape[T.Reg] -1375.5514 1063.990 -1.293 0.196 -3462.211 711.108
LandContour[T.HLS] 8915.2545 3073.450 2.901 0.004 2887.714 1.49e+04
LandContour[T.Lvl] 4916.7337 2411.037 2.039 0.042 188.293 9645.175
LotConfig[T.Inside] -1480.8200 1057.304 -1.401 0.162 -3554.367 592.727
Neighborhood[T.Edwards] -8284.9469 2013.304 -4.115 0.000 -1.22e+04 -4336.526
Neighborhood[T.IDOTRR] -1.371e+04 3458.817 -3.965 0.000 -2.05e+04 -6930.046
Neighborhood[T.NAmes] -6008.2102 1709.653 -3.514 0.000 -9361.121 -2655.299
Neighborhood[T.NridgHt] 2.789e+04 2169.062 12.860 0.000 2.36e+04 3.21e+04
Neighborhood[T.Somerst] 1.499e+04 1880.285 7.974 0.000 1.13e+04 1.87e+04
Neighborhood[T.StoneBr] 2.644e+04 3369.340 7.848 0.000 1.98e+04 3.31e+04
Condition1[T.Norm] 1.064e+04 1681.972 6.325 0.000 7340.458 1.39e+04
Condition1[T.PosN] 1.492e+04 3540.865 4.212 0.000 7970.970 2.19e+04
Condition2[T.Norm] -49.4815 5460.027 -0.009 0.993 -1.08e+04 1.07e+04
Condition2[T.PosN] -4.295e+04 1.35e+04 -3.180 0.001 -6.94e+04 -1.65e+04
Dwelling_Type[T.TwnhsE] 1.024e+04 2553.144 4.012 0.000 5236.497 1.53e+04
HouseStyle[T.1Story] 619.3548 1873.886 0.331 0.741 -3055.643 4294.353
HouseStyle[T.2Story] -3780.8137 1917.995 -1.971 0.049 -7542.318 -19.309
RoofStyle[T.Hip] 1690.4723 1226.274 1.379 0.168 -714.453 4095.398
RoofMatl[T.WdShngl] 1.042e+04 5474.374 1.904 0.057 -312.074 2.12e+04
Exterior1st[T.MetalSd] -8235.5834 5535.797 -1.488 0.137 -1.91e+04 2621.026
Exterior1st[T.Plywood] -1159.7488 5527.049 -0.210 0.834 -1.2e+04 9679.705
Exterior1st[T.VinylSd] -8741.0645 5810.747 -1.504 0.133 -2.01e+04 2654.767
Exterior2nd[T.HdBoard] 3443.8812 4219.797 0.816 0.415 -4831.835 1.17e+04
Exterior2nd[T.MetalSd] 8510.7359 4316.030 1.972 0.049 46.290 1.7e+04
Exterior2nd[T.VinylSd] 1.087e+04 4550.212 2.389 0.017 1944.769 1.98e+04
MasVnrType[T.None] -1274.2169 1183.501 -1.077 0.282 -3595.257 1046.823
MasVnrType[T.Stone] 3305.9509 1947.921 1.697 0.090 -514.242 7126.144
ExterQual[T.Fa] -3828.9733 7220.285 -0.530 0.596 -1.8e+04 1.03e+04
ExterQual[T.Gd] -4239.2877 3141.431 -1.349 0.177 -1.04e+04 1921.577
ExterQual[T.TA] -1.089e+04 3457.092 -3.149 0.002 -1.77e+04 -4107.815
ExterCond[T.TA] 238.0023 3845.841 0.062 0.951 -7304.326 7780.330
Foundation[T.CBlock] 2437.3976 1976.318 1.233 0.218 -1438.487 6313.282
Foundation[T.PConc] 1020.8542 2194.331 0.465 0.642 -3282.591 5324.299
BsmtQual[T.Gd] -1.285e+04 2265.686 -5.672 0.000 -1.73e+04 -8408.576
BsmtQual[T.No Basement] -1.389e+04 4103.430 -3.386 0.001 -2.19e+04 -5845.956
BsmtQual[T.TA] -1.432e+04 2692.971 -5.319 0.000 -1.96e+04 -9043.355
BsmtCond[T.No basement] -1.469e+04 3564.657 -4.121 0.000 -2.17e+04 -7697.860
BsmtCond[T.TA] -5513.2068 2137.403 -2.579 0.010 -9705.006 -1321.407
BsmtExposure[T.Gd] 9462.7377 1933.198 4.895 0.000 5671.417 1.33e+04
BsmtExposure[T.No] -4748.1988 1174.409 -4.043 0.000 -7051.408 -2444.990
BsmtExposure[T.No Basement] -9863.3394 1.44e+04 -0.686 0.493 -3.8e+04 1.83e+04
BsmtFinType1[T.GLQ] 9513.3563 1668.536 5.702 0.000 6241.083 1.28e+04
BsmtFinType1[T.No basement] 1.91e+04 2.56e+04 0.746 0.456 -3.11e+04 6.93e+04
BsmtFinType1[T.Unf] 857.2812 1287.577 0.666 0.506 -1667.870 3382.432
BsmtFinType2[T.Rec] -1.092e+04 2.04e+04 -0.537 0.592 -5.08e+04 2.9e+04
BsmtFinType2[T.Unf] -7507.2070 2.03e+04 -0.370 0.711 -4.73e+04 3.23e+04
Heating[T.Grav] -2326.7430 5322.299 -0.437 0.662 -1.28e+04 8111.161
HeatingQC[T.Fa] -5947.4515 3109.931 -1.912 0.056 -1.2e+04 151.635
HeatingQC[T.Gd] -1932.1179 1437.285 -1.344 0.179 -4750.870 886.634
HeatingQC[T.TA] -3562.4604 1397.771 -2.549 0.011 -6303.720 -821.201
CentralAir[T.Y] 3923.9751 2401.788 1.634 0.102 -786.326 8634.276
Electrical[T.SBrkr] 588.2151 1876.008 0.314 0.754 -3090.945 4267.375
KitchenQual[T.Fa] -2.619e+04 4234.564 -6.185 0.000 -3.45e+04 -1.79e+04
KitchenQual[T.Gd] -1.773e+04 2389.152 -7.423 0.000 -2.24e+04 -1.3e+04
KitchenQual[T.TA] -2.512e+04 2642.193 -9.507 0.000 -3.03e+04 -1.99e+04
Functional[T.Typ] 1.663e+04 2042.323 8.142 0.000 1.26e+04 2.06e+04
FireplaceQu[T.Fa] -2555.9064 5074.945 -0.504 0.615 -1.25e+04 7396.897
FireplaceQu[T.Gd] 1823.0485 3874.968 0.470 0.638 -5776.402 9422.499
FireplaceQu[T.No Fireplace] -1014.8925 4399.602 -0.231 0.818 -9643.237 7613.452
FireplaceQu[T.TA] 1147.8511 3991.360 0.288 0.774 -6679.864 8975.566
GarageType[T.BuiltIn] -2130.6148 2225.456 -0.957 0.338 -6495.101 2233.871
GarageType[T.Detchd] -4455.8961 1470.281 -3.031 0.002 -7339.360 -1572.432
GarageType[T.No Garage] -2759.4344 6761.041 -0.408 0.683 -1.6e+04 1.05e+04
GarageFinish[T.No Garage] 1.732e+04 7645.305 2.265 0.024 2325.558 3.23e+04
GarageFinish[T.RFn] -3942.0785 1325.972 -2.973 0.003 -6542.527 -1341.630
GarageFinish[T.Unf] -1393.2860 1668.825 -0.835 0.404 -4666.127 1879.554
GarageQual[T.No Garage] -2.498e+04 5863.492 -4.261 0.000 -3.65e+04 -1.35e+04
GarageQual[T.TA] -1.95e+04 4999.433 -3.901 0.000 -2.93e+04 -9696.464
GarageCond[T.TA] 4465.7450 3468.501 1.288 0.198 -2336.557 1.13e+04
PavedDrive[T.Y] -881.8291 1995.314 -0.442 0.659 -4794.969 3031.311
Fence[T.No Fence] -1201.9158 1384.764 -0.868 0.386 -3917.666 1513.834
MiscFeature[T.Shed] 4254.3337 2529.973 1.682 0.093 -707.359 9216.027
SaleType[T.New] 2.814e+04 6361.898 4.424 0.000 1.57e+04 4.06e+04
SaleType[T.WD] 1101.1544 2338.566 0.471 0.638 -3485.158 5687.467
SaleCondition[T.Normal] 9558.9590 1739.497 5.495 0.000 6147.519 1.3e+04
SaleCondition[T.Partial] -1.499e+04 6192.042 -2.420 0.016 -2.71e+04 -2842.286
Dwell_Type -117.1537 21.813 -5.371 0.000 -159.932 -74.375
OverallQual 1368.3789 496.475 2.756 0.006 394.709 2342.048
OverallCond 2952.1808 833.812 3.541 0.000 1316.939 4587.422
TotalBsmtSF 23.0983 2.929 7.887 0.000 17.354 28.842
FirstFlrSF -8.1891 4.149 -1.974 0.049 -16.326 -0.052
GrLivArea 53.0754 2.807 18.907 0.000 47.570 58.581
BsmtFullBath -3036.6573 557.987 -5.442 0.000 -4130.961 -1942.353
FullBath 2717.4349 1476.396 1.841 0.066 -178.020 5612.890
HalfBath 3908.6039 1417.090 2.758 0.006 1129.458 6687.750
BedroomAbvGr 503.5063 926.248 0.544 0.587 -1313.018 2320.030
KitchenAbvGr -1.662e+04 3410.508 -4.875 0.000 -2.33e+04 -9936.000
Fireplaces 4498.6900 1744.577 2.579 0.010 1077.287 7920.093
GarageCars 4183.3804 1600.502 2.614 0.009 1044.533 7322.227
GarageArea 28.4207 5.282 5.381 0.000 18.062 38.780
==============================================================================
Omnibus: 330.370 Durbin-Watson: 2.036
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2963.683
Skew: -0.465 Prob(JB): 0.00
Kurtosis: 8.783 Cond. No. 2.23e+05
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.23e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
Condition2
trn["Condition2"].value_counts()
Feedr 1657 PosN 3 Name: Condition2, dtype: int64
trn["Condition2"].replace('Norm', 'Feedr', inplace = True)
trn["Condition2"].value_counts()
Feedr 1657 PosN 3 Name: Condition2, dtype: int64
HouseStyle
trn["HouseStyle"].value_counts()
1.5Fin 1147 2Story 513 Name: HouseStyle, dtype: int64
trn["HouseStyle"].replace('1Story', '1.5Fin', inplace = True)
trn["HouseStyle"].value_counts()
1.5Fin 1147 2Story 513 Name: HouseStyle, dtype: int64
Exterior2nd
trn["Exterior2nd"].value_counts()
AsbShng 975 VinylSd 685 Name: Exterior2nd, dtype: int64
trn["Exterior2nd"].replace('HdBoard', 'AsbShng', inplace = True)
trn["Exterior2nd"].replace('MetalSd', 'AsbShng', inplace = True)
trn["Exterior2nd"].value_counts()
AsbShng 975 VinylSd 685 Name: Exterior2nd, dtype: int64
ExterQual
trn["ExterQual"].value_counts()
TA 1029 Ex 631 Name: ExterQual, dtype: int64
trn["ExterQual"].replace('Gd', 'Ex', inplace = True)
trn["ExterQual"].replace('Fa', 'Ex', inplace = True)
trn["ExterQual"].value_counts()
TA 1029 Ex 631 Name: ExterQual, dtype: int64
BsmtExposure
trn["BsmtExposure"].value_counts()
No 1095 Av 421 Gd 144 Name: BsmtExposure, dtype: int64
trn["BsmtExposure"].replace('No Basement', 'Av', inplace = True)
trn["BsmtExposure"].value_counts()
No 1095 Av 421 Gd 144 Name: BsmtExposure, dtype: int64
BsmtFinType1
trn["BsmtFinType1"].value_counts()
BLQ 1188 GLQ 472 Name: BsmtFinType1, dtype: int64
trn["BsmtFinType1"].replace('Unf', 'BLQ', inplace = True)
trn["BsmtFinType1"].replace('No basement', 'BLQ', inplace = True)
trn["BsmtFinType1"].value_counts()
BLQ 1188 GLQ 472 Name: BsmtFinType1, dtype: int64
HeatingQC
trn["HeatingQC"].value_counts()
Ex 1172 TA 488 Name: HeatingQC, dtype: int64
trn["HeatingQC"].replace('Gd', 'Ex', inplace = True)
trn["HeatingQC"].replace('Fa', 'Ex', inplace = True)
trn["HeatingQC"].value_counts()
Ex 1172 TA 488 Name: HeatingQC, dtype: int64
GarageType
trn["GarageType"].value_counts()
Attchd 1195 Detchd 465 Name: GarageType, dtype: int64
trn["GarageType"].replace('No Garage', 'Attchd', inplace = True)
trn["GarageType"].replace('BuiltIn', 'Attchd', inplace = True)
trn["GarageType"].value_counts()
Attchd 1195 Detchd 465 Name: GarageType, dtype: int64
GarageFinish
trn["GarageFinish"].value_counts()
Fin 1089 RFn 479 No Garage 92 Name: GarageFinish, dtype: int64
trn["GarageFinish"].replace('Unf', 'Fin', inplace = True)
trn["GarageFinish"].value_counts()
Fin 1089 RFn 479 No Garage 92 Name: GarageFinish, dtype: int64
SaleType
trn["SaleType"].value_counts()
COD 1517 New 143 Name: SaleType, dtype: int64
trn["SaleType"].replace('WD', 'COD', inplace = True)
trn["SaleType"].value_counts()
COD 1517 New 143 Name: SaleType, dtype: int64
BsmtCond
trn["BsmtCond"].value_counts()
TA 1491 No basement 93 Gd 76 Name: BsmtCond, dtype: int64
#trn["BsmtCond"].replace('TA', 'Gd', inplace = True)
trn["BsmtCond"].value_counts()
TA 1491 No basement 93 Gd 76 Name: BsmtCond, dtype: int64
SaleCondition
trn["SaleCondition"].value_counts()
Normal 1375 Abnorml 144 Partial 141 Name: SaleCondition, dtype: int64
trn["SaleCondition"].replace('Partial', 'Abnorml', inplace = True)
trn["SaleCondition"].value_counts()
Normal 1375 Abnorml 285 Name: SaleCondition, dtype: int64
model_3 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition', data = trn).fit()
print(model_3.summary())
OLS Regression Results
===============================================================================
Dep. Variable: Property_Sale_Price R-squared: 0.908
Model: OLS Adj. R-squared: 0.905
Method: Least Squares F-statistic: 330.3
Date: Mon, 27 Dec 2021 Prob (F-statistic): 0.00
Time: 02:38:55 Log-Likelihood: -18828.
No. Observations: 1660 AIC: 3.775e+04
Df Residuals: 1611 BIC: 3.802e+04
Df Model: 48
Covariance Type: nonrobust
=============================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------------
Intercept 1.583e+04 1.42e+04 1.114 0.265 -1.2e+04 4.37e+04
Zone_Class[T.FV] 3.253e+04 7838.462 4.150 0.000 1.72e+04 4.79e+04
Zone_Class[T.RL] 3.062e+04 7236.072 4.231 0.000 1.64e+04 4.48e+04
Zone_Class[T.RM] 2.525e+04 7045.921 3.584 0.000 1.14e+04 3.91e+04
LandContour[T.HLS] 1.373e+04 3356.073 4.092 0.000 7149.616 2.03e+04
LandContour[T.Lvl] 7559.6763 2509.513 3.012 0.003 2637.422 1.25e+04
Neighborhood[T.Edwards] -9256.6009 2180.037 -4.246 0.000 -1.35e+04 -4980.595
Neighborhood[T.IDOTRR] -1.536e+04 3756.059 -4.090 0.000 -2.27e+04 -7994.650
Neighborhood[T.NAmes] -6207.5088 1896.264 -3.274 0.001 -9926.912 -2488.106
Neighborhood[T.NridgHt] 2.795e+04 2412.130 11.588 0.000 2.32e+04 3.27e+04
Neighborhood[T.Somerst] 1.463e+04 2119.754 6.901 0.000 1.05e+04 1.88e+04
Neighborhood[T.StoneBr] 2.678e+04 3747.789 7.147 0.000 1.94e+04 3.41e+04
Condition1[T.Norm] 1.07e+04 1843.463 5.805 0.000 7084.618 1.43e+04
Condition1[T.PosN] 1.234e+04 4047.992 3.049 0.002 4404.346 2.03e+04
Condition2[T.PosN] -3.955e+04 1.27e+04 -3.126 0.002 -6.44e+04 -1.47e+04
Dwelling_Type[T.TwnhsE] 9707.7886 2654.211 3.658 0.000 4501.720 1.49e+04
HouseStyle[T.2Story] -4782.5391 1801.143 -2.655 0.008 -8315.369 -1249.709
Exterior2nd[T.VinylSd] 6011.0864 1322.490 4.545 0.000 3417.105 8605.068
ExterQual[T.TA] -7012.3820 1756.923 -3.991 0.000 -1.05e+04 -3566.287
BsmtQual[T.Gd] -1.491e+04 2422.938 -6.156 0.000 -1.97e+04 -1.02e+04
BsmtQual[T.No Basement] -1.102e+04 4234.930 -2.602 0.009 -1.93e+04 -2711.982
BsmtQual[T.TA] -1.631e+04 2866.936 -5.691 0.000 -2.19e+04 -1.07e+04
BsmtCond[T.No basement] -1.236e+04 3801.225 -3.251 0.001 -1.98e+04 -4902.647
BsmtCond[T.TA] -3850.0053 2477.860 -1.554 0.120 -8710.174 1010.163
BsmtExposure[T.Gd] 8561.0111 2188.061 3.913 0.000 4269.266 1.29e+04
BsmtExposure[T.No] -5002.6197 1308.817 -3.822 0.000 -7569.783 -2435.456
BsmtFinType1[T.GLQ] 8453.1112 1524.050 5.546 0.000 5463.782 1.14e+04
KitchenQual[T.Fa] -2.79e+04 4395.959 -6.346 0.000 -3.65e+04 -1.93e+04
KitchenQual[T.Gd] -1.779e+04 2554.751 -6.965 0.000 -2.28e+04 -1.28e+04
KitchenQual[T.TA] -2.58e+04 2876.050 -8.971 0.000 -3.14e+04 -2.02e+04
Functional[T.Typ] 1.826e+04 2206.642 8.274 0.000 1.39e+04 2.26e+04
GarageType[T.Detchd] -4602.4055 1519.836 -3.028 0.002 -7583.468 -1621.343
GarageFinish[T.No Garage] 1.621e+04 4081.455 3.972 0.000 8204.001 2.42e+04
GarageFinish[T.RFn] -2798.5323 1275.744 -2.194 0.028 -5300.825 -296.240
GarageQual[T.No Garage] -2.946e+04 5969.936 -4.934 0.000 -4.12e+04 -1.77e+04
GarageQual[T.TA] -1.871e+04 5243.071 -3.568 0.000 -2.9e+04 -8422.456
SaleType[T.New] 1.494e+04 2665.241 5.605 0.000 9711.565 2.02e+04
SaleCondition[T.Normal] 1.14e+04 1834.404 6.213 0.000 7798.793 1.5e+04
Dwell_Type -105.7768 23.104 -4.578 0.000 -151.094 -60.459
OverallQual 2788.0056 635.391 4.388 0.000 1541.726 4034.285
OverallCond 4456.5227 955.884 4.662 0.000 2581.616 6331.429
TotalBsmtSF 17.8734 2.343 7.630 0.000 13.279 22.468
GrLivArea 53.7170 1.983 27.087 0.000 49.827 57.607
BsmtFullBath -2877.0103 628.190 -4.580 0.000 -4109.167 -1644.854
HalfBath 3420.1728 1463.072 2.338 0.020 550.448 6289.898
KitchenAbvGr -1.53e+04 3700.618 -4.135 0.000 -2.26e+04 -8043.051
Fireplaces 6134.1023 998.570 6.143 0.000 4175.469 8092.736
GarageCars 6587.3430 1802.320 3.655 0.000 3052.204 1.01e+04
GarageArea 20.9521 6.042 3.468 0.001 9.101 32.804
==============================================================================
Omnibus: 271.617 Durbin-Watson: 2.013
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2253.920
Skew: -0.509 Prob(JB): 0.00
Kurtosis: 8.617 Cond. No. 6.49e+04
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.49e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
trn.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1660 entries, 0 to 1659 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 1660 non-null int64 1 Zone_Class 1660 non-null object 2 LotFrontage 1660 non-null float64 3 LotArea 1660 non-null float64 4 Road_Type 1660 non-null object 5 Alley 1660 non-null object 6 Property_Shape 1660 non-null object 7 LandContour 1660 non-null object 8 Utilities 1660 non-null object 9 LotConfig 1660 non-null object 10 LandSlope 1660 non-null object 11 Neighborhood 1660 non-null object 12 Condition1 1660 non-null object 13 Condition2 1660 non-null object 14 Dwelling_Type 1660 non-null object 15 HouseStyle 1660 non-null object 16 OverallQual 1660 non-null int64 17 OverallCond 1660 non-null int64 18 YearBuilt 1660 non-null float64 19 YearRemodAdd 1660 non-null int64 20 RoofStyle 1660 non-null object 21 RoofMatl 1660 non-null object 22 Exterior1st 1660 non-null object 23 Exterior2nd 1660 non-null object 24 MasVnrType 1660 non-null object 25 MasVnrArea 1660 non-null float64 26 ExterQual 1660 non-null object 27 ExterCond 1660 non-null object 28 Foundation 1660 non-null object 29 BsmtQual 1660 non-null object 30 BsmtCond 1660 non-null object 31 BsmtExposure 1660 non-null object 32 BsmtFinType1 1660 non-null object 33 BsmtFinSF1 1660 non-null float64 34 BsmtFinType2 1660 non-null object 35 BsmtFinSF2 1660 non-null int64 36 BsmtUnfSF 1660 non-null float64 37 TotalBsmtSF 1660 non-null float64 38 Heating 1660 non-null object 39 HeatingQC 1660 non-null object 40 CentralAir 1660 non-null object 41 Electrical 1660 non-null object 42 FirstFlrSF 1660 non-null float64 43 SecondFlrSF 1660 non-null float64 44 LowQualFinSF 1660 non-null int64 45 GrLivArea 1660 non-null float64 46 BsmtFullBath 1660 non-null int64 47 BsmtHalfBath 1660 non-null int64 48 FullBath 1660 non-null int64 49 HalfBath 1660 non-null int64 50 BedroomAbvGr 1660 non-null int64 51 KitchenAbvGr 1660 non-null int64 52 KitchenQual 1660 non-null object 53 TotRmsAbvGrd 1660 non-null float64 54 Functional 1660 non-null object 55 Fireplaces 1660 non-null int64 56 FireplaceQu 1660 non-null object 57 GarageType 1660 non-null object 58 GarageYrBlt 1660 non-null float64 59 GarageFinish 1660 non-null object 60 GarageCars 1660 non-null int64 61 GarageArea 1660 non-null float64 62 GarageQual 1660 non-null object 63 GarageCond 1660 non-null object 64 PavedDrive 1660 non-null object 65 WoodDeckSF 1660 non-null float64 66 OpenPorchSF 1660 non-null float64 67 EnclosedPorch 1660 non-null int64 68 ThrSsnPorch 1660 non-null int64 69 ScreenPorch 1660 non-null int64 70 PoolArea 1660 non-null int64 71 PoolQC 1660 non-null object 72 Fence 1660 non-null object 73 MiscFeature 1660 non-null object 74 MiscVal 1660 non-null int64 75 MoSold 1660 non-null int64 76 YrSold 1660 non-null int64 77 SaleType 1660 non-null object 78 SaleCondition 1660 non-null object 79 Property_Sale_Price 1660 non-null float64 dtypes: float64(16), int64(21), object(43) memory usage: 1.0+ MB
trn1 = trn[['Dwell_Type', 'Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2', 'Dwelling_Type',
'HouseStyle', 'OverallQual', 'OverallCond', 'Exterior2nd', 'ExterQual', 'BsmtQual', 'BsmtCond',
'BsmtExposure', 'BsmtFinType1', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
'KitchenQual', 'Functional', 'Fireplaces', 'GarageType', 'GarageFinish', 'GarageCars', 'GarageArea',
'GarageQual', 'SaleType', 'SaleCondition', 'Property_Sale_Price']]
trn1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1660 entries, 0 to 1659 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 1660 non-null int64 1 Zone_Class 1660 non-null object 2 LandContour 1660 non-null object 3 Neighborhood 1660 non-null object 4 Condition1 1660 non-null object 5 Condition2 1660 non-null object 6 Dwelling_Type 1660 non-null object 7 HouseStyle 1660 non-null object 8 OverallQual 1660 non-null int64 9 OverallCond 1660 non-null int64 10 Exterior2nd 1660 non-null object 11 ExterQual 1660 non-null object 12 BsmtQual 1660 non-null object 13 BsmtCond 1660 non-null object 14 BsmtExposure 1660 non-null object 15 BsmtFinType1 1660 non-null object 16 TotalBsmtSF 1660 non-null float64 17 GrLivArea 1660 non-null float64 18 BsmtFullBath 1660 non-null int64 19 HalfBath 1660 non-null int64 20 KitchenAbvGr 1660 non-null int64 21 KitchenQual 1660 non-null object 22 Functional 1660 non-null object 23 Fireplaces 1660 non-null int64 24 GarageType 1660 non-null object 25 GarageFinish 1660 non-null object 26 GarageCars 1660 non-null int64 27 GarageArea 1660 non-null float64 28 GarageQual 1660 non-null object 29 SaleType 1660 non-null object 30 SaleCondition 1660 non-null object 31 Property_Sale_Price 1660 non-null float64 dtypes: float64(4), int64(8), object(20) memory usage: 415.1+ KB
test1 = test[['Dwell_Type', 'Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2', 'Dwelling_Type',
'HouseStyle', 'OverallQual', 'OverallCond', 'Exterior2nd', 'ExterQual', 'BsmtQual', 'BsmtCond',
'BsmtExposure', 'BsmtFinType1', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
'KitchenQual', 'Functional', 'Fireplaces', 'GarageType', 'GarageFinish', 'GarageCars', 'GarageArea',
'GarageQual', 'SaleType', 'SaleCondition', 'Property_Sale_Price']]
test1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 413 entries, 1660 to 2072 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Dwell_Type 413 non-null int64 1 Zone_Class 413 non-null object 2 LandContour 413 non-null object 3 Neighborhood 413 non-null object 4 Condition1 413 non-null object 5 Condition2 413 non-null object 6 Dwelling_Type 413 non-null object 7 HouseStyle 413 non-null object 8 OverallQual 413 non-null int64 9 OverallCond 413 non-null int64 10 Exterior2nd 413 non-null object 11 ExterQual 413 non-null object 12 BsmtQual 413 non-null object 13 BsmtCond 413 non-null object 14 BsmtExposure 413 non-null object 15 BsmtFinType1 413 non-null object 16 TotalBsmtSF 413 non-null float64 17 GrLivArea 413 non-null float64 18 BsmtFullBath 413 non-null int64 19 HalfBath 413 non-null int64 20 KitchenAbvGr 413 non-null int64 21 KitchenQual 413 non-null object 22 Functional 413 non-null object 23 Fireplaces 413 non-null int64 24 GarageType 413 non-null object 25 GarageFinish 413 non-null object 26 GarageCars 413 non-null int64 27 GarageArea 413 non-null float64 28 GarageQual 413 non-null object 29 SaleType 413 non-null object 30 SaleCondition 413 non-null object 31 Property_Sale_Price 413 non-null float64 dtypes: float64(4), int64(8), object(20) memory usage: 103.4+ KB
Test data in two parts
x_test = test1.loc[:, test1.columns != 'Property_Sale_Price']
y_test = test1.loc[:, test1.columns == 'Property_Sale_Price']
Predictions on test data
pred_1 = model_3.predict(x_test)
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) ~\anaconda3\lib\site-packages\patsy\categorical.py in categorical_to_int(data, levels, NA_action, origin) 345 try: --> 346 out[i] = level_to_int[value] 347 except KeyError: KeyError: 'Partial' During handling of the above exception, another exception occurred: PatsyError Traceback (most recent call last) ~\anaconda3\lib\site-packages\statsmodels\base\model.py in predict(self, exog, transform, *args, **kwargs) 1076 try: -> 1077 exog = dmatrix(design_info, exog, return_type="dataframe") 1078 except Exception as exc: ~\anaconda3\lib\site-packages\patsy\highlevel.py in dmatrix(formula_like, data, eval_env, NA_action, return_type) 289 eval_env = EvalEnvironment.capture(eval_env, reference=1) --> 290 (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env, 291 NA_action, return_type) ~\anaconda3\lib\site-packages\patsy\highlevel.py in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type) 166 if design_infos is not None: --> 167 return build_design_matrices(design_infos, data, 168 NA_action=NA_action, ~\anaconda3\lib\site-packages\patsy\build.py in build_design_matrices(design_infos, data, NA_action, return_type, dtype) 887 if factor_info not in factor_info_to_values: --> 888 value, is_NA = _eval_factor(factor_info, data, NA_action) 889 factor_info_to_isNAs[factor_info] = is_NA ~\anaconda3\lib\site-packages\patsy\build.py in _eval_factor(factor_info, data, NA_action) 83 assert factor_info.type == "categorical" ---> 84 result = categorical_to_int(result, factor_info.categories, NA_action, 85 origin=factor_info.factor) ~\anaconda3\lib\site-packages\patsy\categorical.py in categorical_to_int(data, levels, NA_action, origin) 358 level_str = "[%s]" % (", ".join(level_strs)) --> 359 raise PatsyError("Error converting data to categorical: " 360 "observation with value %r does not match " PatsyError: Error converting data to categorical: observation with value 'Partial' does not match any of the expected levels (expected: ['Abnorml', 'Normal']) Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition ^^^^^^^^^^^^^ During handling of the above exception, another exception occurred: PatsyError Traceback (most recent call last) <ipython-input-1350-12d4354fb999> in <module> ----> 1 pred_1 = model_3.predict(x_test) ~\anaconda3\lib\site-packages\statsmodels\base\model.py in predict(self, exog, transform, *args, **kwargs) 1082 '\n\nThe original error message returned by patsy is:\n' 1083 '{0}'.format(str(str(exc)))) -> 1084 raise exc.__class__(msg) 1085 if orig_exog_len > len(exog) and not is_dict: 1086 if exog_index is None: PatsyError: predict requires that you use a DataFrame when predicting from a model that was created using the formula api. The original error message returned by patsy is: Error converting data to categorical: observation with value 'Partial' does not match any of the expected levels (expected: ['Abnorml', 'Normal']) Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition ^^^^^^^^^^^^^
Residuals
y_test_series = y_test.iloc[:, 0]
resd_1 = y_test_series - pred_1
make histogram of residuals
And check whether its approximately bell-shaped, symmetrical
plt.figure(figsize=(12,10))
sns.distplot(trn1.resd_1, color='skyblue')
plt.title('Histogram of Residuals')
plt.ylabel('Prob')
plt.xlabel('Residuals')
plt.show()
Make scatter plots,
x = each continuous predictor one by one
y = response variable
see whether a linear relationship is visible?
trn1.info()
numerical_trn1 = trn1.select_dtypes(include=np.number)
print(len(numerical_trn1.columns))
numerical_trn1.columns
categorical_trn1 = trn1.select_dtypes(include = 'O')
print(len(categorical_trn1.columns))
categorical_trn1.columns
### With Good Predictors
a = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
'Fireplaces', 'GarageCars', 'GarageArea', 'Property_Sale_Price']
trn1_pp = trn1[a]
sns.pairplot(trn1_pp ,palette = sns.color_palette('Set3_r'), markers = ['>'], plot_kws = {'facecolors':'#00FF00'})
# visualize the relationship between the features and the response using scatterplots
sns.pairplot(trn1, x_vars = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
'Fireplaces', 'GarageCars', 'GarageArea'],
y_vars = 'Property_Sale_Price', size=7, aspect=0.7)
Make scatter plot,
x = obsno
y = residuals
if, no geometric pattern is visible, assumption of INDEPENDENCE OF ERROR is holding good and not violated
# Now We Create Observation Number
obs = np.arange(1660)
obs
obsno = pd.DataFrame(obs) # convert array into a data frame
trn1['obsno'] = obsno # craete a new variable/column 'obsno' in yr data
plt.figure(figsize=(12,10))
sns.scatterplot(x = trn1.obsno, y = trn1.resd_1, palette='spring_r', color='darkslateblue', markers='+')
plt.title('Scatterplot of obsno vs residuals')
Make scatter plot,
x = predict
y = residuals
if, no geometric pattern is visible, assumption of homoscadasticity is holding good and not violated
plt.figure(figsize=(12,10))
sns.scatterplot(x = trn1.predict, y = trn1.resd_1, palette='spring', color='teal')
plt.title('Scatterplot of predict vs residuals')
trn1.info()
x = trn1[['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath',
'KitchenAbvGr', 'Fireplaces', 'GarageCars', 'GarageArea']]
x.columns
vif_trn = pd.DataFrame()
vif_trn["feature"] = x.columns
# calculating VIF for each feature
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif_trn["VIF"] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]
print(vif_trn)
# import numpy and statsmodels for durbin watson
import numpy as np
from statsmodels.stats.stattools import durbin_watson
gfg = durbin_watson(model_3.resid)
print("Durbin Watson Value :", round(gfg, 3))
se_1 = resd_1.pow(2)
sse_1 = se_1.sum()
msse_1 = sse_1/len(se_1)
import math
math.sqrt(msse_1)
obsno = np.arange(1660)
fig, ax = plt.subplots()
ax.plot(obsno, y_test_series, 'o', label='Data')
ax.plot(obsno, pred_1, 'r-', label='Predicted')
ax.legend(loc='best')